CN105787420B

CN105787420B - Method and device for biometric authentication and biometric authentication system

Info

Publication number: CN105787420B
Application number: CN201410816701.2A
Authority: CN
Inventors: 冯雪涛; 张超; 刘洋; 金尚骏; 裵致成
Original assignee: Beijing Samsung Telecommunications Technology Research Co Ltd; Samsung Electronics Co Ltd
Current assignee: Beijing Samsung Telecommunications Technology Research Co Ltd; Samsung Electronics Co Ltd
Priority date: 2014-12-24
Filing date: 2014-12-24
Publication date: 2020-07-14
Anticipated expiration: 2034-12-24
Also published as: KR20160078208A; KR102367481B1; CN105787420A

Abstract

The application discloses a method and a device for biometric authentication and a biometric authentication system. One embodiment of the method comprises: receiving at least two biometric signals; extracting the same physiological characteristic from the at least two biological characteristic signals respectively; and processing the physiological characteristic to determine whether the at least two biometric signals are from the same real organism. The embodiment realizes the judgment of the authenticity of the organism to be authenticated or identified, and improves the safety of the identity authentication system.

Description

Method and device for biometric authentication and biometric authentication system

Technical Field

The present application relates to the field of computer technologies, and in particular, to a method, an apparatus, and a system for biometric authentication.

Background

With the progress of sensor manufacturing technology, pattern recognition and machine learning technology, biometric recognition technology has been widely popularized and developed. However, in order to prevent identity authentication using stolen or forged biometrics, an identity authentication system needs to have a live body detection function to confirm that biometrics are from a live real individual.

In the prior art, a plurality of software or hardware-based living body detection methods for identity authentication systems exist, wherein a method using combination of a plurality of biological characteristics has better anti-counterfeiting capability, especially when the biological characteristics comprise signals (such as electrocardiosignals) from the living body which are difficult to forge and copy. However, counterfeiting or copying of various biometrics separately and then using them simultaneously can still fool the authentication system into passing authentication. Therefore, there is a need to further enhance the security of identity authentication systems.

Disclosure of Invention

The application provides a method, an apparatus and a biometric authentication system for biometric authentication.

In one aspect, the present application provides a method of biometric authentication, the method comprising: receiving at least two biometric signals; extracting the same physiological characteristic from the at least two biological characteristic signals respectively; and processing the physiological characteristic to determine whether the at least two biometric signals are from the same real organism.

In some alternative implementations, the determination is made based on a difference between a correspondence between the same physiological characteristic from at least two biometric signals acquired simultaneously by the same organism and a correspondence between the same physiological characteristic from at least two biometric signals acquired by different organisms or at different times being distinguishable.

In a further implementation, the determining includes: calculating a measure of coherence between the physiological features of the at least two biometric signals based on the attributes of the physiological features; and in response to the consistency measure satisfying a preset condition, confirming that the at least two biometric signals are from the same real organism.

In some implementations, calculating the consistency metric includes: and calculating consistency measurement according to the corresponding relation of the acquisition time of the preset physiological phenomenon in the waveform of the physiological characteristic.

In some alternative implementations, the predetermined physiological phenomenon corresponds to a peak or a trough in the waveform of the physiological characteristic, and the measure of consistency is characterized by a degree of difference or a degree of similarity, wherein the degree of difference is expressed as a variance of an acquisition time deviation of the corresponding peak or trough in the waveform of the physiological characteristic, and the degree of similarity is expressed as an inverse of the degree of difference.

In other implementations, calculating the consistency metric includes: the consistency metric is calculated using a regressor, wherein the regressor is trained via the input physiological characteristic data and the set consistency metric.

In other alternative implementations, the determining includes: classifying the at least two biological characteristic signals by utilizing a classifier based on the attribute of the physiological characteristic, wherein the classifier is obtained by training two types of samples, the first type of sample is from the biological characteristic signals of the same organism which are acquired at the same time, and the second type of sample is from the biological characteristic signals which are acquired at different times or from different organisms; and confirming whether the at least two biological characteristic signals are from the same real organism according to the classification result.

In further implementations, the attributes of the physiological characteristics include at least one of: time domain attributes, frequency domain attributes and statistical attributes.

In further implementations, the time-domain attribute includes an occurrence time, a change time, a duration of a predetermined physiological phenomenon in the physiological characteristic, or a signal waveform of the physiological characteristic; the frequency domain property comprises a signal frequency or spectral distribution of the physiological characteristic.

In further implementations, the physiological characteristic is a time-varying physiological characteristic. In further implementations, the physiological characteristic includes heartbeat and/or respiration.

In some optional implementations, the method for biometric authentication further comprises: and performing identity authentication or identification based on the judgment result of whether the at least two biological characteristic signals are from the same real organism.

In a further implementation manner, the performing identity authentication or identification based on the determination result of whether the at least two biometric signals are from the same real organism includes: matching the identity characteristic information extracted from the at least two biological characteristic signals with registered identity characteristic information; and in response to a successful match and the determination confirming that the at least two biometric signals are from the same real organism, authenticating or identifying the identity of the organism.

In further implementations, the identity characteristic information includes at least one of: face images, fingerprint images, palm print images, blood vessel images, iris images, retina images, voice signals, gait characteristics, signature or handwriting characteristics, electrocardiosignals and electroencephalogram signals.

In some alternative implementations, the at least two biometric signals are acquired simultaneously.

In further implementations, the acquiring lasts for a predetermined period of time.

In some optional implementations, the biometric signal includes at least one of: the human body electronic device comprises a human face image, a fingerprint image, a palm print image, a blood vessel image, an iris image, a retina image, an electrocardiosignal, an electroencephalogram signal, a photoplethysmography (PPG) signal, a blood pressure signal, a heart sound signal, a human body modulated electromagnetic wave signal, a chest or abdomen motion signal and a human body conductivity signal.

In a second aspect, the present application provides an apparatus for biometric authentication, the apparatus comprising: a receiving unit configured to receive at least two biometric signals; an extraction unit configured to extract the same physiological feature from the at least two kinds of biometric signals, respectively; and a determination unit configured to process the physiological characteristic to determine whether the at least two biometric signals are from the same real organism. The apparatus may further comprise means or an arrangement configured to perform the steps of the embodiments of the method according to the first aspect of the present application.

In a third aspect, the present application provides a biometric authentication system comprising a sensor and a processor, the sensor configured to acquire at least two biometric signals; and the processor is configured to receive at least two biometric signals, extract the same physiological characteristic from the at least two biometric signals, respectively, and process the physiological characteristic to determine whether the at least two biometric signals are from the same real organism.

In some implementations, the sensor is configured to acquire the at least two biometric signals simultaneously. The processor may be further configured to perform the steps of embodiments of the method according to the first aspect of the application.

According to the method, the device and the system for biometric authentication, the at least two biometric characteristic signals are received, the same physiological characteristic is extracted from the at least two biometric characteristic signals respectively, and the physiological characteristic is processed finally to judge whether the at least two biometric characteristic signals are from the same real organism, so that the authenticity of the organism to be authenticated or identified is judged, and the safety of the identity authentication system is improved.

Drawings

Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:

fig. 1 shows an exemplary flowchart of a method for biometric authentication according to an embodiment of the application;

FIG. 2 illustrates an exemplary implementation flow of determining whether at least two biometric signals are from the same real object according to an embodiment of the present application;

FIG. 3 illustrates another exemplary implementation flow of determining whether at least two biometric signals are from the same real object according to an embodiment of the present application;

fig. 4 shows another exemplary flowchart of a method for biometric authentication according to an embodiment of the application;

FIG. 5 illustrates an exemplary implementation flow of identity authentication or identification based on at least two biometric signals according to an embodiment of the present application;

fig. 6 is a schematic structural diagram illustrating an embodiment of an apparatus for biometric authentication provided according to an embodiment of the present application;

fig. 7 is a schematic structural diagram illustrating an embodiment of a biometric authentication system provided according to an embodiment of the present application; and

fig. 8 a-8 f illustrate some exemplary implementations of biometric authentication systems according to embodiments of the present application.

Detailed Description

The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.

It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.

Referring to fig. 1, an exemplary flowchart 100 of a method for biometric authentication according to an embodiment of the present application is shown. The embodiment is mainly explained by applying the method to an identity authentication system with various biological characteristic acquisition and processing functions. The method for biometric authentication includes the steps of:

as shown in fig. 1, in step 110, at least two biometric signals are received.

A biometric is a feature that characterizes a physical or behavioral characteristic of an organism, such as a human face, a fingerprint, a palm print, a blood vessel, an iris, a retina, an electrocardiogram, an electroencephalogram, a pulse, a blood pressure, a heart sound, chest or abdominal motion, a human conductivity, and the like. Accordingly, the biometric signals may include face images, fingerprint images, palm print images, blood vessel images, iris images, retina images, electrocardiosignals, electroencephalogram signals, photoplethysmography (PPG) signals, blood pressure signals, heart sound signals, body modulated electromagnetic wave signals, chest or abdomen movement signals, body conductivity signals, and other signals not listed that include biometric features. Those skilled in the art will appreciate that the above examples are non-exhaustive and that there may be other various biometric signals now known or developed in the future.

The biometric signal can be collected in various forms such as an electrical signal, a sound signal, a force signal, an electromagnetic signal, an image or video signal, an optical signal, and the like. The sensor used to acquire the biometric signal may take a variety of forms. For example, a color image sensor may be used to collect a face image, a fingerprint image, a palm print image, a retina image, etc.; the infrared image sensor can be used for acquiring images of biological characteristics sensitive to an infrared light source, such as blood vessel images; the vibration sensor can be used for acquiring signals with vibration characteristics, such as chest or abdomen movement signals. Other sensors such as pressure sensors may be used to acquire pressure-generating biometric signals such as blood pressure signals, chest or abdomen movement signals, and the like. The multiple biometric signals may be collected by multiple different sensors at the same time or by a single sensor that integrates multiple functions. For example, the face image and the PPG signal may be simultaneously acquired by a color image sensor and a photosensor, respectively, or the face image and the PPG signal may be simultaneously acquired by a multi-function sensor integrated with the color image sensor and the photosensor.

In some implementations, each biometric signal may be acquired for a predetermined period of time to obtain a continuous biometric signal over time or a plurality of discrete data points of a biometric characteristic that are correlated in time (e.g., acquiring an electrocardiographic signal waveform within 1 minute, or acquiring multiple face images over 10 seconds). The acquired biometric signals may be transmitted to the processor after processing such as: converting continuous analog signals (such as electrocardiosignals, PPG signals and the like) into digital signals, processing noise in the signals (for example, removing image noise of eyelids, eyelashes and the like from acquired iris images), and processing the intensity, distribution, change and the like of the signals (for example, normalizing palm print images). The processor may then receive the processed biometric signal.

Next, in step 120, the same physiological characteristic is extracted from the at least two biometric signals, respectively.

In the present embodiment, the physiological characteristic is a characteristic representing a physiological state of a living body, and may be a heartbeat, a respiration, a blood pressure, a body temperature, or the like. To prevent spoofing using false bio-signals, in some embodiments the extracted physiological features may be time-varying physiological features such as heartbeat, respiration, etc. If more than two biometric signals are received in step 110, the processor may extract the same physiological characteristic from each of the received biometric signals. A plurality of corresponding physiological features may also be extracted from each biometric signal (e.g., heartbeat and respiration are extracted simultaneously for a plurality of biometric signals).

The physiological characteristics can be extracted by a variety of methods. In some implementations, the physiological characteristic may be derived based on a direct mapping relationship between the acquired biometric signal and the physiological characteristic. For example, the respiratory rate of a human body has a relatively stable proportional relationship (1:4) with the pulse rate, and can be estimated from the PPG signal. In other implementations, the physiological characteristic may be derived based on a relationship between a time-varying characteristic of the biometric signal over a period of time and the physiological characteristic. For example, the heart beat has a direct corresponding relation with the color change of the capillary vessels under the skin of the human face, so that the change of the skin color along with the time can be analyzed from a plurality of human face images collected in a time sequence, and the heart beat can be obtained according to the change.

In the present embodiment, the extracted physiological characteristics may be represented in various forms. Such as vectors, sets of vectors, signal waveforms, etc. In some alternative implementations, the extracted physiological characteristic is represented as a vector, each element in the vector may correspond to a signal acquisition time, and the value of an element may represent the intensity of the physiological characteristic or the position in which it is located during its variation. In particular, in practical applications, the physiological characteristics may be represented using vectors of the form, for example: using a one-dimensional vector

To represent the heart beat, where N is the number of samples, N ≧ 1, a₁Corresponding to the first sampling time point, a₁Can represent the strength of the electrocardiosignal at the first sampling time point, and so on, a_NCorresponding to the Nth sampling time point, a_NCan represent the strength of the electrocardiosignal at the Nth sampling time point. Further, each element in the vector may also correspond to a time instant when a physiological characteristic changes. E.g. vectors representing heart beats

Element a in (1)₁Time of the first peak, and so on, a_NRepresenting the time of the nth peak. In other alternative implementations, the extracted physiological characteristics may be represented by a set of vectors, where each vector corresponds to a time when a physiological characteristic changes and a degree of the change. For example, a set of vectors may be employed

(M ≧ 1) for the heartbeat, the first vector thereof

May correspond to the moment of occurrence of the first peak of the heartbeat, a₁₂The intensity of the ECG signal corresponding to the first peak, and so on, a_M1May correspond to the occurrence of the Mth peak of the heartbeat, a_M2May correspond to the ecg signal strength of the mth peak. Furthermore, the extracted physiological characteristic can also be represented by a continuous signal waveform in a two-dimensional coordinate system, wherein the variation trend of the waveform can correspond to the variation trend of the intensity of the physiological characteristic.

Then, in step 130, the physiological characteristic is processed to determine whether the at least two biometric signals are from the same real organism.

The determination of whether the at least two biometric signals are from the same real object may be based on a difference between the physiological characteristic representation of the at least two biometric signals from the same organism taken simultaneously and the physiological characteristic representation of the at least two biometric signals from different organisms or taken at different times. In other words, since the consistency between the same physiological characteristics of at least two biometric signals from the same organism collected simultaneously and the consistency between the same physiological characteristics of at least two biometric signals from different organisms or collected at different times have distinguishable differences, it is possible to determine whether the received at least two biometric signals are from the same real object based on the above principle. For example, there is a large difference between the heartbeat frequencies obtained based on the facial images of the same person at different times (for example, at the time of a relaxed state and at the time of a stressed state), and there is also a large difference between the heartbeat frequencies obtained based on the facial images of different persons. Thus, it may be determined whether the at least two biometric signals are from the same real object based on this difference. In particular, when the difference is strongly present, it can be confirmed that the biometric signal is not from the same real organism, i.e. the collected biometric signal may be a false, simulated or copied signal.

In this embodiment, when the biometric system is used for authentication or identification, the system may perform living body detection on the authenticatee or the identified person based on the physiological characteristics, that is, detect whether the authenticatee or the identified person is a real living body, and may authenticate the authenticatee or the identified person as a real living body if the same physiological characteristics included in the at least two biometric signals collected by the sensor show consistency.

After extracting the physiological features contained in the at least two biometric signals, the physiological features may be processed as follows: form conversion, feature analysis, similarity analysis, and the like. For example, when the at least two extracted biometric signals have different representations, they may be first converted into signals having the same representation (for example, a heartbeat expressed in a vector group form extracted from the PPG signal and a heartbeat expressed in a signal waveform form extracted from the face image are both converted into a heartbeat expressed in a one-dimensional vector form), and whether the at least two biometric signals are from the same real organism is determined based on the converted physiological features. For another example, the extracted physiological features may be subjected to feature analysis, and when the at least two extracted biological signals are represented in the form of a vector group, the vector group may be converted into a matrix, and the extracted physiological features may be subjected to feature analysis by calculating eigenvalues or covariance matrices of the matrix, or the like.

In addition, the similarity or difference analysis can be performed on the physiological characteristics contained in the extracted at least two biological characteristic signals, and the specific implementation manner will be described in detail in the following embodiments.

In this embodiment, whether the signals are from the same real organism can be determined based on the physiological features of the extracted at least two biological feature signals. Specifically, if the extracted physiological features have the same intensity, frequency or variation tendency, it can be confirmed that the at least two kinds of biometric signals are from the same organism.

According to the embodiment of the application, the processor receives at least two biological characteristic signals, then the same physiological characteristic is respectively extracted from the biological characteristic signals, and then the extracted physiological characteristic is processed to judge whether the biological characteristic signals are from the same real organism, so that multiple biological characteristics copied at different times or on different organisms cannot be used at the same time, the difficulty of biological characteristic embezzlement is obviously increased, the judgment of the authenticity of the organism to be authenticated or identified is realized, and the safety of the identity authentication system is improved.

With further reference to fig. 2, an exemplary implementation flow 200 of determining whether at least two biometric signals are from the same real object according to an embodiment of the present application is illustrated, i.e. a flow chart illustrating an exemplary implementation of the method step 130 of fig. 1 is illustrated. In this embodiment, by performing similarity or difference analysis on the physiological features included in the extracted at least two biological feature signals, a determination is made as to whether the signals are from real organisms and from the same organism.

As shown in fig. 2, in step 210, a measure of coherence between the physiological features of the at least two biometric signals is calculated based on the attributes of the physiological features.

The consistency measure is an index for characterizing consistency between at least two physiological characteristics and may be calculated using a variety of methods. The measure of consistency may be characterized by a degree of similarity or a degree of difference. For example, when the extracted physiological characteristic is represented in the form of a signal waveform, the consistency metric may be calculated from a correspondence of acquisition times of a predetermined physiological phenomenon in the waveform of the physiological characteristic. In this example, the predetermined physiological phenomenon may correspond to a peak or a trough in a waveform of the physiological characteristic. After the wave peak or the wave trough time is respectively detected from the signal waveforms of the physiological characteristics of the two biological signals, the corresponding relation of the two groups of wave peak or wave trough time can be obtained according to the signal acquisition time. The measure of consistency may be characterized by a degree of difference or a degree of similarity. When characterized by a degree of dissimilarity, the degree of dissimilarity may be expressed as a variance of the acquisition time deviations of corresponding peaks or troughs in the waveform of the physiological characteristic. When characterized by similarity, the similarity may be expressed as the inverse of the degree of difference described above. If two biological characteristic signals are acquired simultaneously and from the same organism, the deviation corresponding to the peak or trough time is basically constant and does not change along with time, the variance is small, the difference is small, the similarity is large, namely the consistency is high; however, if two kinds of biological characteristic signals are collected at different times or from different organisms, the deviation corresponding to the peak or trough time is unstable, and changes violently with time, the variance is large, the difference is large, the similarity is small, and the consistency is low.

Alternatively, the consistency metric may be calculated by machine learning. For example, the consistency metric may be calculated using a regressor trained via the consistency metrics of the input physiological characteristic data and settings. Specifically, a regressor may be generated using artificially generated or collected physiological characteristic data and an artificially set consistency metric as a training sample set, with a higher consistency metric being set for physiological characteristics from biometric signals collected simultaneously during training and a lower consistency metric being set for physiological characteristics of biometric signals collected non-simultaneously or from different organisms. Then, data points with fixed length can be sampled from vectors or signal waveforms of physiological features of different biological feature signals respectively and sent to the regressor, and the output result is the consistency measurement.

In some optional implementations of the present embodiment, the consistency metric may be calculated based on at least one of the following attributes of the physiological characteristic: time domain attributes, frequency domain attributes, and statistical attributes.

In some optional implementations, the time domain attributes may specifically include an occurrence time (e.g., a peak time, a trough time, etc. in an electrocardiogram), a variation time, a duration, or a signal waveform of the physiological characteristic of the predetermined physiological phenomenon; the frequency domain property may include a signal frequency or a signal spectrum of the physiological characteristic.

Then, in step 220, in response to the consistency measure satisfying a preset condition, at least two biometric signals are confirmed to be from the same real organism.

In this embodiment, whether the signals are from the same real organism is determined based on the consistency measure. Generally, there is a high consistency between physiological characteristics of the same real organism at the same time, and there is a low consistency between physiological characteristics of different organisms or collected at different times. As mentioned previously, the consistency metric may be characterized by a degree of similarity or a degree of difference. Accordingly, a threshold value may be set for the consistency measure of the physiological characteristics of the same real living organism at the same time. If the consistency measure calculated in step 210 meets a predetermined condition, for example, the similarity exceeds a first predetermined threshold or the difference is lower than a second predetermined threshold, the biometric signal including the physiological characteristic may be determined to be from the same real organism, otherwise, the biometric signal including the physiological characteristic may be determined not to be from the same real organism or not to be collected at the same time, wherein one or more biometric signals may be fake or duplicated false signals, and the authentication system may prevent the organism using the false signals from passing authentication.

In some optional implementations of the present implementation, the setting of the threshold may be based on training results of a certain number of sample sets, or may be manually set according to an empirical value. The setting of the threshold based on the training result of the sample set may specifically be performed as follows: firstly, selecting a consistency measurement sample set of physiological features of the same real organism and consistency measurement sample sets of physiological features acquired by different organisms or different moments, selecting a plurality of consistency measurement values in a certain range, respectively calculating the distribution density or the distribution quantity of the consistency measurement of the physiological features of the same real organism on the consistency measurement values and the distribution density or the distribution quantity of the consistency measurement of the physiological features acquired by different organisms and different moments aiming at each consistency measurement value, obtaining a distribution curve of the consistency measurement of the physiological features of the real organism and a distribution curve of the consistency measurement of the physiological features acquired by different organisms or different moments, and selecting the consistency measurement corresponding to an intersection point as a preset threshold value if the two curves are intersected; if the two curves do not intersect, a value between the minimum consistency metric value of the physiological characteristic of the same real organism and the maximum consistency metric value of the physiological characteristic acquired by different organisms or different moments can be selected as the preset threshold value.

With further reference to fig. 3, there is shown another exemplary implementation flow 300 of determining whether at least two biometric signals are from the same real object according to an embodiment of the present application, i.e. a flow chart illustrating another exemplary implementation of the method step 130 of fig. 1.

As shown in fig. 3, in step 310, at least two biometric signals are classified using a classifier based on attributes of the physiological features.

In this embodiment, a classifier may be used to classify two types of biometric signals, where the classifier is trained using two types of samples, a first type of sample is derived from biometric signals of the same organism collected at the same time, and a second type of sample is derived from biometric signals collected at different times or biometric signals from different organisms. The sample size may be determined based on training time and training result accuracy, the features input to the classifier may be attributes of processed physiological features, such as normalized time domain attributes, frequency domain attributes, etc., the classifier may employ an algorithm of a support vector machine, and in some implementations, in order to improve the classification accuracy, a strong classifier may be formed using cascaded weak classifiers to classify the biological feature signals.

Similarly, the attributes of the physiological characteristics may include at least one of: time domain attributes, frequency domain attributes, and statistical attributes. In some optional implementations, the time domain attributes may specifically include an occurrence time (e.g., a peak time, a trough time, etc. in an electrocardiogram), a variation time, a duration, or a signal waveform of the physiological characteristic of the predetermined physiological phenomenon; the frequency domain property may include a signal frequency or a signal spectrum of the physiological characteristic.

Then, in step 320, it is determined whether the at least two biometric signals are from the same real organism based on the classification result.

If the classifier outputs a class identical to the class I of the trained samples, it can be confirmed that the at least two biometric signals are from the same real organism, otherwise, the at least two biometric signals are considered to be from different organisms or collected at different times.

With further reference to fig. 4, another exemplary flow chart 400 of a method for biometric authentication according to an embodiment of the present application is shown.

As shown in fig. 4, at step 410, at least two biometric signals are received.

In this embodiment, the processor may receive biometric signals from the sensors, and these signals may be acquired simultaneously by a plurality of sensors through optical imaging, signal detection, or the like, or may be acquired simultaneously by a sensor integrating a plurality of functions via a plurality of signal acquisition methods.

Next, in step 420, the same physiological characteristic is extracted from the at least two biometric signals, respectively.

In this embodiment, the same physiological feature is extracted for each kind of biometric signal, and since there is a relationship between the physiological feature and the biometric signal (for example, there is a direct correspondence between the heart rate and the change of the skin color), the relationship between the biometric signal and the physiological feature can be analyzed based on the relationship, and then the physiological feature is extracted from the biometric signal according to the relationship. The extracted physiological features may be represented in a variety of forms, such as vectors, sets of vectors, signal waveforms, and the like.

Next, in step 430, the physiological characteristic is processed to determine whether the at least two biometric signals are from the same real organism.

After the physiological features are extracted, the physiological features need to be processed to determine whether the physiological features are from the same real organism. The form conversion and normalization processing may be performed on the biometric features to judge the authenticity of the various biometric features based on the same form and criteria. The physiological characteristics may also be subjected to feature analysis and similarity analysis to confirm whether the biometric signals are from the same real organism.

It is understood that the implementation of

steps

410, 420, and 430 in this embodiment may be the same as

steps

110, 120, and 130 in the foregoing embodiments, and will not be described herein again.

Then, in step 440, authentication or identification is performed based on the at least two biometric signals. Specifically, the identity authentication or identification is performed based on the determination result of whether the at least two biometric signals are from the same real organism.

In this embodiment, the determination result of step 430 is used as one of the bases of identity authentication or identification, and the identity authentication or identification is performed on the to-be-authenticated or the to-be-identified person. The biometric signal may contain characteristic information, such as identity information, that distinguishes the organism from other organisms. Specifically, when it is determined in step 430 that at least two biometric signals are from the same real organism, the identity authentication or identification of the to-be-authenticated or identified person can be performed based on the identity characteristic information in the biometric signals. Alternatively, the identity of the authenticated person or the authenticated person may be determined by combining a plurality of authentication or identification results based on the plurality of biometric signals for identity authentication or identification, respectively.

With further reference to fig. 5, an exemplary implementation flow 500 of identity authentication or identification based on at least two biometric signals, i.e. a flow chart illustrating an exemplary implementation of the method step 440 of fig. 4, according to an embodiment of the present application is shown.

As shown in fig. 5, in step 510, identity feature information is extracted from at least one of at least two biometric signals.

The identity characteristic information has stronger identification degree so as to distinguish the organism from other organisms. Typically, a feature having uniqueness and stability is used as the identity information. Some optional identity information may include, but is not limited to: face images, fingerprint images, palm print images, blood vessel images, iris images, retina images, voice signals, gait characteristics, signature or handwriting characteristics, electrocardiosignals and electroencephalogram signals. Those skilled in the art will appreciate that the above examples are non-exhaustive and that there may be other various identity characteristic information now known or developed in the future. In this embodiment, to authenticate or identify the identity of a living organism, identity characteristic information of the living organism is first extracted from a biometric signal.

There are various ways to extract the identity information. The received biological characteristic signal can be directly used as identity characteristic information, for example, a face image is used as identity verification information for identity authentication; or extracting the biological characteristic signal, representing the identity verification information by the extracted characteristic, for example, analyzing the walking posture and the step frequency of the person from the continuous walking image of the person, and extracting the gait characteristic by adopting a filtering mode and the like as the identity verification information; the change rule can be quantized and represented as the identity authentication information by analyzing the change rule of a certain characteristic in the biological characteristic signal within a period of time, for example, the electrocardiosignal within a period of time is recorded, and the change rule of the occurrence time of the wave crest of the electrocardiosignal (such as the occurrence interval, the difference between each occurrence time and the last wave crest value) is quantized and represented as the identity authentication information.

In some optional implementations of the embodiment, the identity characteristic information may be extracted from one of the biometric signals for identity authentication or identification. In other alternative implementations, the identity feature information may be extracted from each biometric signal, and the identity feature information extracted from the multiple biometric signals may be the same type of identity feature information or different types of identity feature information.

It is understood that the identity characteristic information may be the same as or different from the biometric characteristic extracted in the biometric authenticity judgment. When the two are the same, step 510 may be omitted and the results of the previous extraction may be directly utilized.

Next, in step 520, the identity information is matched with the registered identity information.

In this embodiment, the extracted identity feature information and the identity feature information registered in the database may be matched in two modes: and (4) authentication and identification. In the authentication mode, matching the extracted identity characteristic information with identity characteristic information of a certain expected organism in a database; in the recognition mode, all the identity characteristic information in the database can be traversed, and the organism corresponding to the identity characteristic information with the highest matching degree can be searched.

Before the identity characteristic information is matched, the extracted identity characteristic information can be preprocessed. For example, iris recognition may be performed by first segmenting and normalizing an iris region from an acquired eye image, then performing denoising, enhancement and other processing on the normalized image, then extracting texture features of the iris by using methods such as filtering, and then performing template matching in an iris database. Alternatively, the iris features may be represented by vectors, and template matching may be achieved by calculating similarity measures between the extracted iris feature vectors and template vectors stored in the database, where the similarity measures may include euclidean distance, hamming distance, squared difference, correlation coefficient, and the like.

Then, in step 530, in response to the matching being successful and the determination result confirming that the at least two biometric signals are from the same real organism, the identity of the organism is authenticated or recognized.

In this embodiment, if it is confirmed that at least two kinds of biometric signals are from the same real object, the identity authentication system is allowed to authenticate or recognize the identity of the living object, and further, the identity of the living object is authenticated or recognized according to the matching result of step 520. Specifically, in the authentication mode, if the extracted identity characteristic information matches the identity characteristic information of a certain expected organism in the database, the organism can be confirmed to be the expected organism, otherwise, the organism can be confirmed to be other organisms different from the expected organism; in the identification mode, the identity information corresponding to the identity characteristic information that is most highly matched with the identity characteristic information of the organism to be identified in the database may be used as the identity information of the organism to be identified.

In some alternative implementations, the determination result confirms that at least two kinds of biometric signals are from the same real organism and a plurality of identity feature information of the same or different types are extracted in step 510, and the above step 502 may be performed for each identity verification information to authenticate or identify the identity of the organism based on a plurality of matching results.

As can be seen from fig. 4, different from the embodiment corresponding to fig. 1, the exemplary process 400 of this embodiment adds a step 440 of performing identity authentication or identification based on at least two biometric signals, and through the addition of the step 440, when the acquired biometric signal is from a real individual, the identity of the real individual can be further authenticated and identified.

The embodiment of the application can realize judgment of the false biological characteristic signal and identity authentication or identification of the real organism, and enhances the safety of the identity authentication system. In performing such authentication or identification, it is required to simultaneously acquire at least two biometric signals. Further, the acquisition may be continued for a predetermined period of time in order to acquire a time-varying physiological characteristic.

With further reference to fig. 6, a schematic structural diagram 600 of an embodiment of the apparatus for biometric authentication provided in accordance with an embodiment of the present application is shown.

As shown in fig. 6, the apparatus 600 for biometric authentication includes: a receiving unit 610 configured to receive at least two biometric signals; an extracting unit 620 configured to extract the same physiological feature from the at least two biometric signals respectively; and a determination unit 630 configured to process the physiological characteristic to determine whether the at least two biometric signals are from the same real organism.

In the present embodiment, the receiving unit 610 may receive biometric signals from sensors, and these signals may be acquired by a plurality of sensors through optical imaging, signal detection, or the like, or may be acquired simultaneously by a sensor integrating a plurality of functions via a plurality of signal acquisition methods. Then, the extracting unit 620 extracts the same physiological feature for each kind of biological feature signal, respectively, and since the physiological feature has a correlation with the biological feature signal (for example, the heart rate can be reflected in the change of skin color), the relationship between the biological feature signal and the physiological feature can be analyzed based on the correlation, and then the physiological feature is extracted from the biological feature signal according to the relationship. The extracted physiological features may be represented in a variety of forms, such as vectors, sets of vectors, signal waveforms, and the like. The determination unit 630 may then process the physiological characteristics and determine whether they are from the same real organism. The judgment unit 630 may perform form conversion and normalization processing on the biometric features so as to judge the authenticity of the plurality of biometric features based on the same form and standard. The physiological characteristics may also be subjected to feature analysis and similarity analysis to confirm whether the biometric signals are from the same real organism.

In some alternative implementations, the determining unit 630 may perform the determining operation based on a difference that a correspondence between the same physiological characteristic representation of the at least two biometric signals from the same organism collected simultaneously and a correspondence between the same physiological characteristic representation of the at least two biometric signals from different organisms or collected at different times have a distinguishable difference.

In a possible determination manner, the determining unit 630 is specifically configured to calculate a consistency metric between the physiological features of the at least two biometric signals based on the attributes of the physiological features; and in response to the consistency measure satisfying a preset condition, confirming that the at least two biometric signals are from the same real organism.

In a possible implementation manner, the determining unit 630 is specifically configured to calculate the consistency metric according to a corresponding relationship between acquisition times of predetermined physiological phenomena in the waveform of the physiological characteristic.

Wherein the predetermined physiological phenomenon corresponds to a peak or a trough in the waveform of the physiological characteristic, and the measure of consistency is characterized by a degree of difference, expressed as a variance of a deviation in acquisition time of the corresponding peak or trough in the waveform of the physiological characteristic, or a degree of similarity, expressed as a reciprocal of the degree of difference.

In another possible implementation manner, the determining unit 630 is specifically configured to calculate the consistency metric by using a regressor, where the regressor is trained by using the input physiological characteristic data and the set consistency metric.

In another possible determination manner, the determining unit 630 is specifically configured to classify the at least two biological feature signals by using a classifier based on the attribute of the physiological feature, where the classifier is obtained by training two types of samples, a first type of sample is obtained from biological feature signals of the same organism acquired at the same time, and a second type of sample is obtained from biological feature signals acquired at different times or biological feature signals from different organisms; and confirming whether the at least two biological characteristic signals are from the same real organism according to the classification result.

In some optional implementations of the present embodiment, the apparatus 600 for biometric authentication may further include an authentication and identification unit (not shown) configured to perform identity authentication or identification based on at least two biometric signals, wherein a determination result of whether the at least two biometric signals are from the same real organism is used as one of basis of the identity authentication or identification.

Specifically, the authentication and identification unit is specifically configured to match the identity feature information extracted from the at least two biometric signals with registered identity feature information; and in response to a successful match and the determination confirming that the at least two biometric signals are from the same real organism, authenticating or identifying the identity of the organism.

The units described in the embodiments of the present application may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a receiving unit, an extracting unit, and a judging unit. Where the names of the units do not in some cases constitute a limitation of the unit itself, for example, a receiving unit may also be described as a "unit for receiving at least two biometric signals".

The method for biometric authentication provided by the application can be used in identity authentication systems with various composite biological characteristics, so that on the other hand, the application also provides a biometric authentication system. Fig. 7 is a schematic structural diagram illustrating an embodiment of a biometric authentication system provided according to an embodiment of the present application. As shown in fig. 7, the biometric authentication system 700 includes a sensor 710 and a processor 730. Sensor 710 is configured to acquire at least two biometric signals; the processor 730 is configured to receive the at least two biometric signals from the sensors, extract the same physiological characteristic from the at least two biometric signals, respectively, and process the physiological characteristic to determine whether the at least two biometric signals are from the same real organism. In some embodiments, the sensor is configured to acquire at least two biometric signals simultaneously.

The biometric authentication system 700 may further include a signal conversion circuit 720.

In some implementations, the sensor 710 may collect the biometric signal for a predetermined period of time to obtain a continuous biometric signal or a plurality of discrete data points of the biometric that are correlated in time over time. The acquired biometric signals may be processed by the signal conversion circuit 720, such as the following, converted to a form that may be processed by the processor 730, and then transmitted to the processor 730: converting continuous analog signals into digital signals, processing noise in the signals, and processing intensity, distribution, variation, and the like of the signals.

The biometric authentication system 700 may also include a memory 740 configured to store instructions, parameters, data, etc. used in processing the biometric signals or to record data and results obtained during system operation.

The biometric authentication system 700 may further comprise an output device 750 for outputting the result of the processing by the processor. Such as displaying instructions for use, displaying whether the signal is authentic, displaying the result of authentication or identification, operating other equipment, software, etc.

The biometric authentication system 700 described above can be implemented on a variety of devices that require authentication or identification. The method can be used for judging whether the authenticatee is a real organism or not, verifying whether the authenticatee and the registrant are the same organism or not, and identifying a certain organism from a plurality of registrants.

As an example, fig. 8 a-8 f illustrate some exemplary implementations of a biometric authentication system according to embodiments of the present application.

As shown in fig. 8a, it shows a smart watch 810 with identity authentication function based on electrocardio signals and PPG signals. Electrocardiograph (ECG)

electrodes

812 and 814 are respectively installed on the front surface and the back surface of the watch and used for acquiring electrocardiosignals; the PPG sensor 813 is used to acquire PPG signals. The processor can receive the acquired electrocardiosignals and the PPG signals, extract heartbeat or respiratory characteristics from the electrocardiosignals and the PPG signals, analyze the heartbeat or respiratory characteristics, judge whether the signals are acquired by the same user at the same moment according to the consistency of the extracted heartbeat or respiratory characteristics, verify the identity of the user, and display the judgment and verification results on the screen 811.

Fig. 8b shows a smartphone 820 with identity authentication function based on face images and electrocardiosignals. As shown in fig. 8b, a screen 821 is used to display the collected signals and the processing results; the camera 822 may be used to collect a face image;

ECG electrodes

823 and 824 may acquire cardiac electrical signals. The processor of the mobile phone can receive the collected face image and the electrocardiosignal, extract heartbeat features from the face image and the electrocardiosignal, process the extracted heartbeat features, and judge the authenticity and further verify the identity of the user based on the processed heartbeat features.

Fig. 8c shows an automobile 830 with identity authentication based on a human face image, an electrocardiosignal and a tension sensor. As shown in fig. 8c, the camera 831 on the automobile rearview mirror is used for acquiring a face image; an ECG electrode 832 is arranged on the steering wheel and used for collecting electrocardiosignals, and a tension sensor 833 is arranged on the safety belt and can also be used for collecting the electrocardiosignals. The processor can receive the collected signals, extract heartbeat or respiration characteristics from each signal respectively, process the extracted heartbeat or respiration characteristics, and judge whether the signals are from the same real organism or not based on consistency of the processed heartbeat or respiration characteristics.

Fig. 8d shows a door lock system 840 with identity authentication based on a face image and a fingerprint (or palm print, blood vessel) image. As shown in fig. 8d, the camera 841 above the door collects the face image, and the door lock is provided with a sensor 842 for collecting the fingerprint, vein and palm print images.

Fig. 8e shows a smartphone 850 based on face images and heartbeat sensors. As shown in fig. 8e, the mobile phone camera 852 is used for acquiring a face image; the earphone is provided with a pulse detector 853. When the user performs identity authentication, the image sensor of the mobile phone camera 852 acquires a face image, and the processor can receive the acquired face image and extract heartbeat features from the face image; the pulse sensor 853 located on the earplug obtains the heartbeat characteristics, and the processor analyzes the two heartbeat characteristics and makes a judgment on the authenticity of the organism to be authenticated. The acquired image and the pulse signal may be displayed on the screen 851.

Fig. 8f shows a bracelet and headset 860 with identity authentication based on electrocardiosignals and electroencephalogram signals. When the user performs identity authentication, an ECG sensor 861 positioned on the bracelet collects electrocardiosignals; an EEG sensor 862 is mounted on the earphone and used for collecting EEG signals. When the user performs identity authentication, the processor can receive the electrocardiosignals and the electroencephalogram signals, then extract heartbeat features from the electrocardiosignals and the electroencephalogram signals, transmit the extracted heartbeat features to the same device in a wireless communication mode, and then the device judges whether the extracted heartbeat is from the same real organism or not and can further perform identity authentication or identification on the organism.

As another aspect, the present application also provides a computer-readable storage medium, which may be the computer-readable storage medium included in the apparatus in the above-described embodiments; or it may be a separate computer-readable storage medium not incorporated in the terminal. The computer readable storage medium stores one or more programs for use by one or more processors in performing the methods for biometric authentication described herein.

The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by a person skilled in the art that the scope of the invention as referred to in the present application is not limited to the embodiments with a specific combination of the above-mentioned features, but also covers other embodiments with any combination of the above-mentioned features or their equivalents without departing from the inventive concept. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.

Claims

1. A method for biometric authentication, the method comprising:

receiving at least two biometric signals;

extracting the same physiological characteristic from the at least two biological characteristic signals respectively; and

processing the physiological characteristic to determine whether the at least two biometric signals are from the same real organism;

wherein the determination is made based on a difference that the correspondence between the same physiological characteristics of at least two biometric signals from the same organism collected simultaneously and the correspondence between the same physiological characteristics of at least two biometric signals from different organisms or collected at different times are distinguishable;

the consistency is calculated as follows: calculating a correspondence between the same physiological characteristic of the at least two biometric signals using a regressor, wherein the regressor is trained on a correspondence metric of settings and a first sample of physiological characteristics from the same biometric signal acquired at the same time and a second sample of physiological characteristics from a biometric signal acquired at a different time or from a different organism.

2. The method of claim 1, wherein the determining comprises:

calculating a measure of coherence between the physiological features of the at least two biometric signals based on the attributes of the physiological features; and

in response to the consistency measure satisfying a preset condition, confirming that the at least two biometric signals are from the same real organism.

3. The method of claim 2, wherein the computing the consistency metric comprises:

and calculating consistency measurement according to the corresponding relation of the acquisition time of the preset physiological phenomenon in the waveform of the physiological characteristic.

4. The method of claim 3, wherein the predetermined physiological phenomenon corresponds to a peak or trough in the waveform of the physiological characteristic, and the measure of consistency is characterized by a degree of difference, expressed as a variance of a deviation in acquisition time of the corresponding peak or trough in the waveform of the physiological characteristic, or a degree of similarity, expressed as a reciprocal of the degree of difference.

5. The method of claim 1, wherein the determining comprises:

classifying the at least two biological characteristic signals by utilizing a classifier based on the attribute of the physiological characteristic, wherein the classifier is obtained by training two types of samples, the first type of sample is from the biological characteristic signals of the same organism which are acquired at the same time, and the second type of sample is from the biological characteristic signals which are acquired at different times or from different organisms; and

and confirming whether the at least two biological characteristic signals are from the same real organism according to the classification result.

6. The method according to any one of claims 2-5, wherein the attributes of the physiological characteristics include at least one of: time domain attributes, frequency domain attributes and statistical attributes.

7. The method of claim 6,

the time domain attribute comprises the occurrence time, the change time, the duration time of a predetermined physiological phenomenon in the physiological characteristic or the signal waveform of the physiological characteristic; and

the frequency domain property comprises a signal frequency or spectral distribution of the physiological characteristic.

8. The method of claim 1, wherein the physiological characteristic is a time-varying physiological characteristic.

9. The method of claim 1, wherein the physiological characteristic comprises heartbeat and/or respiration.

10. The method of claim 1, further comprising:

and performing identity authentication or identification based on the judgment result of whether the at least two biological characteristic signals are from the same real organism.

11. The method of claim 10, wherein the performing identity authentication or identification based on the determination of whether the at least two biometric signals are from the same real organism comprises:

matching the identity characteristic information extracted from the at least two biological characteristic signals with registered identity characteristic information;

and in response to a successful match and the determination confirming that the at least two biometric signals are from the same real organism, authenticating or identifying the identity of the organism.

12. The method of claim 11, wherein the identity information comprises at least one of: face images, fingerprint images, palm print images, blood vessel images, iris images, retina images, voice signals, gait characteristics, signature or handwriting characteristics, electrocardiosignals and electroencephalogram signals.

13. The method of claim 1, wherein the at least two biometric signals are acquired simultaneously.

14. The method of claim 13, wherein the acquiring lasts for a predetermined period of time.

15. The method according to any one of claims 1-5, 7-14, wherein the biometric signal comprises at least one of: the human face image, the fingerprint image, the palm print image, the blood vessel image, the iris image, the retina image, the electrocardiosignal, the electroencephalogram signal, the photoplethysmography signal, the blood pressure signal, the heart sound signal, the electromagnetic wave signal modulated by the human body, the chest or abdomen motion signal and the human body conductivity signal.

16. An apparatus for biometric authentication, the apparatus comprising:

a receiving unit configured to receive at least two biometric signals;

an extraction unit configured to extract the same physiological feature from the at least two kinds of biometric signals, respectively; and

a determining unit configured to process the physiological characteristic to determine whether the at least two biometric signals are from the same real organism;

wherein the determination unit is further configured to perform a determination operation based on a difference that a correspondence between the same physiological characteristics of at least two biometric signals acquired simultaneously from the same organism and a correspondence between the same physiological characteristics of at least two biometric signals acquired from different organisms or at different times have a distinguishable difference;

17. The apparatus of claim 16, wherein the determining unit is further configured to:

18. The apparatus of claim 17, wherein the determining unit is further configured to:

19. The apparatus of claim 18, wherein the predetermined physiological phenomenon corresponds to a peak or a trough in the waveform of the physiological characteristic, and the measure of coherence is characterized by a degree of dissimilarity expressed as a variance of a time deviation of acquisition of the corresponding peak or trough in the waveform of the physiological characteristic or a degree of similarity expressed as a reciprocal of the degree of dissimilarity.

20. The apparatus of claim 16, wherein the determining unit is further configured to:

21. The apparatus according to any one of claims 17-20, wherein the attribute of the physiological characteristic comprises at least one of: time domain attributes, frequency domain attributes and statistical attributes.

22. The apparatus of claim 21,

23. The device of claim 16, wherein the physiological characteristic is a time-varying physiological characteristic.

24. The device of claim 16, wherein the physiological characteristic comprises heartbeat and/or respiration.

25. The apparatus of claim 16, further comprising:

and the authentication and identification unit is configured for performing identity authentication or identification based on the judgment result of whether the at least two biological characteristic signals are from the same real organism.

26. The apparatus of claim 25, wherein the authentication and identification unit is further configured to:

27. The apparatus of claim 26, wherein the identity information comprises at least one of: face images, fingerprint images, palm print images, blood vessel images, iris images, retina images, voice signals, gait characteristics, signature or handwriting characteristics, electrocardiosignals and electroencephalogram signals.

28. The apparatus of claim 16, wherein the at least two biometric signals are acquired simultaneously.

29. The apparatus of claim 28, wherein the acquisition lasts for a predetermined period of time.

30. The apparatus according to any of claims 16-20, 22-29, wherein the biometric signal comprises at least one of: the human face image, the fingerprint image, the palm print image, the blood vessel image, the iris image, the retina image, the electrocardiosignal, the electroencephalogram signal, the photoplethysmography signal, the blood pressure signal, the heart sound signal, the electromagnetic wave signal modulated by the human body, the chest or abdomen motion signal and the human body conductivity signal.

31. A biometric authentication system comprising a sensor and a processor,

the sensor is configured to acquire at least two biometric signals; and is

The processor is configured to receive the at least two biometric signals, extract a same physiological characteristic from the at least two biometric signals, respectively, and process the physiological characteristic to determine whether the at least two biometric signals are from a same real organism;

32. The system of claim 31, wherein the determining comprises:

33. The system of claim 32, wherein the computing the consistency metric comprises:

34. The system of claim 33, wherein the predetermined physiological phenomenon corresponds to a peak or a trough in the waveform of the physiological characteristic, and the measure of consistency is characterized by a degree of difference, expressed as a variance in a time deviation of acquisition of the corresponding peak or trough in the waveform of the physiological characteristic, or a degree of similarity, expressed as a reciprocal of the degree of difference.

35. The system of claim 31, wherein the determining comprises:

36. The system according to any one of claims 32-35, wherein the attributes of the physiological characteristic include at least one of: time domain attributes, frequency domain attributes and statistical attributes.

37. The system of claim 36,

38. The system of claim 31, wherein the physiological characteristic is a time-varying physiological characteristic.

39. The system of claim 31, wherein the physiological characteristic comprises heartbeat and/or respiration.

40. The system of claim 31, further comprising:

41. The system according to claim 40, wherein said performing identity authentication or identification based on the determination of whether the at least two biometric signals are from the same real organism comprises:

42. The system of claim 41, wherein the identity information comprises at least one of: face images, fingerprint images, palm print images, blood vessel images, iris images, retina images, voice signals, gait characteristics, signature or handwriting characteristics, electrocardiosignals and electroencephalogram signals.

43. The system of claim 31, wherein the sensor is configured to acquire the at least two biometric signals simultaneously.

44. The system of claim 43, wherein the acquisition lasts for a predetermined period of time.

45. The system according to any of claims 31-35, 37-44, wherein the biometric signal comprises at least one of: the human face image, the fingerprint image, the palm print image, the blood vessel image, the iris image, the retina image, the electrocardiosignal, the electroencephalogram signal, the photoplethysmography signal, the blood pressure signal, the heart sound signal, the electromagnetic wave signal modulated by the human body, the chest or abdomen motion signal and the human body conductivity signal.

46. An electronic device, comprising:

one or more processors;

a storage device having one or more programs stored thereon;

the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-15.

47. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-15.