CN114419713B - Auxiliary method for face recognition, face recognition method and terminal device - Google Patents
Auxiliary method for face recognition, face recognition method and terminal device Download PDFInfo
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Abstract
The application provides a face recognition auxiliary method, a face recognition method and terminal equipment, wherein the face recognition auxiliary method comprises the steps of obtaining a face image to be recognized; and executing reminding operation corresponding to the face distribution based on the face distribution in the face image when the face distribution is abnormal.
Description
The patent application is a divisional application of Chinese patent application with the application number 2018110274913, the application date 2018, 9 and 4, and the invention name of 'an auxiliary method for face recognition, a face recognition method and terminal equipment'.
Technical Field
The embodiment of the specification relates to the technical field of face recognition, in particular to a face recognition assisting method, a face recognition method and terminal equipment.
Background
With the rapid development of various payment technologies, face payments have been made in order to greatly simplify the payment process. Face payment is a new electronic payment mode, and the payment mode consists of two parts, namely, face recognition logging in a user wallet account and deduction from the wallet to complete the payment process. The face recognition login process of the user wallet account is to scan and/or shoot a face picture of the user, compare the face picture with a reserved picture in the user wallet account to complete identification and verification of the user identity, and therefore payment is completed by deducting money from the wallet. However, in the current face payment mode, a plurality of interference factors can exist in the process of scanning and/or shooting the face picture of the user to influence the selection of the real user, so that the accuracy of the user selection is influenced, and the safety is poor.
Disclosure of Invention
The embodiment of the specification provides a face recognition auxiliary method, a face recognition method and terminal equipment, which are used for prompting a guiding user to remove interference factors, so that the accuracy of face image selection of the user is improved.
The embodiment of the specification adopts the following technical scheme:
in a first aspect, there is provided an assistance method for face recognition, including:
acquiring a face image to be recognized;
Acquiring face distribution in the face image;
and when the face distribution is abnormal, executing reminding operation corresponding to the face distribution based on the face distribution in the face image.
In a second aspect, a face recognition method is provided, including:
acquiring an image comprising a plurality of faces;
selecting at least one face image from the images of the plurality of faces;
comparing the at least one face image with a face image of the target user;
And determining whether the identification is successful or not based on the comparison result.
In a third aspect, there is provided a terminal device comprising:
the first acquisition module is used for acquiring a face image to be identified;
the second acquisition module is used for acquiring face distribution in the face image;
And the execution module is used for executing reminding operation corresponding to the face distribution based on the face distribution in the face image when the face distribution is abnormal.
In a fourth aspect, there is provided a terminal device, including:
the acquisition module is used for acquiring images comprising a plurality of faces;
A selection module for selecting at least one face image from the plurality of face images;
The comparison module is used for comparing the face image with the face image of the target user based on the at least one face image;
and the determining module is used for determining whether the identification is successful or not based on the comparison result.
In a fifth aspect, there is provided a terminal device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program when executed by the processor implementing the steps of:
acquiring a face image to be recognized;
Acquiring face distribution in the face image;
and when the face distribution is abnormal, executing reminding operation corresponding to the face distribution based on the face distribution in the face image.
In a sixth aspect, there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring a face image to be recognized;
Acquiring face distribution in the face image;
and when the face distribution is abnormal, executing reminding operation corresponding to the face distribution based on the face distribution in the face image.
In a seventh aspect, there is provided a terminal device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program when executed by the processor implementing the steps of:
acquiring an image comprising a plurality of faces;
selecting at least one face image from the images of the plurality of faces;
comparing the at least one face image with a face image of the target user;
And determining whether the identification is successful or not based on the comparison result.
In an eighth aspect, there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring an image comprising a plurality of faces;
selecting at least one face image from the images of the plurality of faces;
comparing the at least one face image with a face image of the target user;
And determining whether the identification is successful or not based on the comparison result.
The above at least one technical scheme adopted by the embodiment of the application can achieve the following beneficial effects:
According to the embodiment of the application, the face distribution in the face image is obtained, and the reminding operation corresponding to the face distribution is executed based on the face distribution in the face image when the face distribution is abnormal, so that a user can adjust according to the reminding to remove interference factors, and the accuracy of the face image selection of the user is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
fig. 1 is a flowchart of an auxiliary method for face recognition according to an embodiment of the present disclosure;
Fig. 2 is a schematic implementation diagram of an actual application scenario of an auxiliary method for face recognition according to an embodiment of the present disclosure;
fig. 3 is a system block diagram of an actual application scenario of an auxiliary method for face recognition according to an embodiment of the present disclosure;
Fig. 4 is a flowchart of a face recognition method according to an embodiment of the present disclosure;
fig. 5 is one of the block diagrams of the terminal device provided in one embodiment of the present disclosure;
Fig. 6 is a second block diagram of a terminal device according to an embodiment of the present disclosure;
fig. 7 is a third block diagram of a terminal device according to an embodiment of the present disclosure;
fig. 8 is a fourth block diagram of a terminal device according to an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The embodiment of the application provides a face recognition auxiliary method and terminal equipment, which are used for prompting a guiding user to remove interference factors, so that the accuracy of face image selection of the user is improved. The embodiment of the application provides an auxiliary method for face recognition, and an execution subject of the method can be but is not limited to terminal equipment or a device or a system capable of being configured to execute the method provided by the embodiment of the application.
For convenience of description, hereinafter, embodiments of the method will be described taking an execution body of the method as an example of a terminal device capable of executing the method. It will be appreciated that the subject of execution of the method is a terminal device which is merely an exemplary illustration and should not be construed as limiting the method.
Fig. 1 is a flowchart of an auxiliary method for face recognition according to an embodiment of the present application, where the method of fig. 1 may be performed by a terminal device, as shown in fig. 1, and the method may include:
Step 110, acquiring a face image to be recognized.
The implementation manner of acquiring the face image to be identified may be to acquire the face image to be identified by a scanning manner or acquire the face image to be identified by a shooting manner. The embodiment of the present application is not particularly limited.
And 120, acquiring face distribution in the face image.
The method for acquiring the face distribution in the face image can be specifically realized by detecting whether the acquired face image contains a face or not through a preset algorithm, detecting the number of faces if the face image contains the face, and detecting whether the acquired face image contains an incomplete face image or not through the preset algorithm.
And 130, executing reminding operation corresponding to the face distribution based on the face distribution in the face image when the face distribution is abnormal.
The abnormal face distribution may include at least one of the absence of a face in the face image, the presence of at least two faces in the face image, the presence of at least two largest-sized faces in the face image, and the characteristic information of the faces in the face image not satisfying a predetermined condition.
According to the embodiment of the application, the face distribution in the face image is obtained, and the reminding operation corresponding to the face distribution is executed based on the face distribution in the face image when the face distribution is abnormal, so that a user can adjust according to the reminding to remove interference factors, and the accuracy of the face image selection of the user is improved.
Alternatively, as an embodiment, step 120 may be specifically implemented as:
taking the face image as input of a multi-face detection model to obtain output face distribution;
the multi-face detection model is obtained by training based on face image samples with abnormal face distribution.
The face image sample with abnormal face distribution can comprise at least one of a face image sample without a face, a face image sample with at least two faces and a face image sample with incomplete faces.
It is assumed that the face image samples having abnormal face distribution include a face image sample having no face and a face image sample having at least two faces, and the face image samples having normal face distribution include a face image sample having one face.
The multi-face detection model can be obtained by firstly, roughly including 3 types of face image samples in training data, respectively selecting one thousand images under the same category for a face image sample without a face, a face image sample with at least two faces and a face image sample with one face, and then training the 3 categories for one thousand face image samples to obtain the multi-face detection model. How to train and obtain the multi-face detection model through one thousand face image samples of 3 categories belongs to the prior art, and the embodiment of the application is not repeated.
According to the embodiment of the application, the multi-face detection model is obtained through training the face image sample with abnormal face distribution, the face image is used as the input of the multi-face detection model to obtain the output face distribution, whether the interference factor exists in the face image is determined according to whether the face distribution is abnormal or not, the influence of the interference factor exists in the acquired face image is effectively avoided, the success rate of user identity authentication and face payment by adopting the face image in the follow-up process is further ensured, and the usability and safety of lose face payment flow are ensured.
Optionally, as an embodiment, if the face distribution abnormality includes that no face exists in the face image, executing a reminding operation corresponding to the face distribution, including:
and reminding a user to be positioned in an image acquisition area of an image acquisition device of the terminal equipment based on the fact that no human face exists in the human face image.
It should be understood that after step 120 is performed, the face recognition assisting method provided by the embodiment of the present application further includes determining whether the face distribution is abnormal, that is, determining whether there are faces in the face image, if there are at least two faces in the face image, determining whether there are at least two faces in the face image, and if there are no faces, prompting the user to locate in an image acquisition area of an image acquisition device of the terminal device.
In the embodiment of the application, if no human face exists in the human face image, a reminding operation corresponding to human face distribution is executed to remind a user that the human face is not detected and guide the user to adjust the standing position so as to accurately acquire the human face image of the user.
It should be appreciated that when there are multiple faces in the image acquisition region, there may or may not be interference with the face recognition.
Optionally, as an embodiment, if the face distribution abnormality includes that at least two faces exist in the face image, executing a reminding operation corresponding to the face distribution, including:
and prompting a user to ensure that no other user exists in the acquisition area of the image acquisition device of the terminal equipment if at least two faces exist in the face image and the difference between the largest two face sizes is smaller than a preset threshold value.
It should be understood that after step 120 is performed, the face recognition assistance method provided by the embodiment of the present application further includes determining whether at least two faces exist in the face image, and if so, determining whether a difference between sizes of two largest faces in the face image is greater than a predetermined threshold. If it is determined that the difference between the two faces with the largest sizes in the face image is greater than a preset threshold, reminding operation is to remind a user to ensure that no other user operation exists in the acquisition area of the image acquisition device of the terminal equipment. For example, the user is reminded of "please ensure that there are no other people around you when brushing the face in order to ensure payment security".
For example, if the user stands side by side with other users (such as friends, relatives or strangers) in the image acquisition area of the image acquisition device of the terminal device, there are at least two faces with the largest size in the face image acquired by the image acquisition device.
According to the embodiment of the application, when two largest faces with similar sizes exist in the face image, other users are reminded of existing around the user, and the user is guided to clear the other users around the user, so that the accuracy of the acquired face image is ensured, and the payment safety of lose face is ensured.
Optionally, as an embodiment, if the face distribution abnormality includes that at least two faces exist in the face image, executing a reminding operation corresponding to the face distribution, including:
selecting the face with the largest size in the face image;
determining whether the feature information of the face with the largest size meets the face recognition comparison condition;
If the distance between the terminal equipment and the image acquisition device is not satisfied, reminding the user to keep a preset distance from the terminal equipment.
For example, if the user stands in the image acquisition area of the image acquisition device of the terminal device with other users (such as friends, relatives or strangers) and back and forth, there are at least two faces in the face image acquired by the image acquisition device. The at least two faces are different in size due to different distances from the image acquisition device. At this time, since the user is located at the nearest position of the image capturing device in actual operation, the size of the face image of the user is usually the largest.
The feature information of the face may refer to size information of the face, position information of the face on a terminal screen, and the like. Accordingly, the face recognition comparison condition is used for representing the condition that the face recognition comparison is satisfied, for example, the face recognition comparison condition is the size range of the face.
The face with the largest size is the face with the largest weighted size, and the face with the largest weighted size is obtained based on the size of a face detection frame and the distance from the center of the face detection frame to the center of a screen of the terminal equipment. It should be understood that, based on the size of the face detection frame, the result of the face detection frame center to the screen center distance of the terminal device after the face detection frame center to the screen center distance is inversely weighted (i.e., the smaller the face detection frame center to the screen center distance is weighted the greater the distance is weighted the smaller the distance), and the mathematical expression is that the weighted face size = the face detection frame size/the face detection frame center to the screen center distance.
The embodiment of the application selects the face with the largest size in the face image, determines whether the characteristic information of the face with the largest size meets the face recognition comparison condition, if not, reminds the user of keeping a preset distance from the image acquisition device of the terminal equipment so as to remind the user that the face image selected by the user does not meet the face recognition comparison condition, and guides the user to adjust the position of the image acquisition area of the image acquisition device and the distance from the image acquisition device so as to encourage the user to be positioned at the appointed position of the image acquisition area of the image acquisition device when the user is lose face so as to facilitate the subsequent processing.
Optionally, as an embodiment, when a detection result of the specified detection operation is normal, the face recognition assisting method provided by the embodiment of the present application may further include:
determining whether a face exists in the face image, if so, determining whether characteristic information of the face meets the face recognition comparison condition, if not, reminding a user of keeping a preset distance from an image acquisition device of the terminal equipment, and if so, sending the face image to be recognized to the recognition terminal equipment.
It can be understood that whether the characteristic information of the face meets the face recognition comparison condition is determined, and if so, the face image to be recognized is sent to the recognition terminal device. The identification terminal equipment can compare the face image with a pre-stored face image, and if the similarity value of the face image and the pre-stored face image is larger than a preset value, the identification terminal equipment determines that the user identity authentication passes and deducts money from a wallet to finish payment operation. The predetermined value needs to be set according to actual requirements, and the embodiment of the application is not particularly limited.
The identification terminal equipment can be used for comparing the face image with a pre-stored face image, and can be specifically realized by acquiring the image information of a face area of the face image and the image information of the face area of the pre-stored face image, comparing the two image information, and determining the similarity value of the face image and the pre-stored face image based on the similar characteristics in the two image information. The pre-stored face image may be a face image corresponding to a user wallet account stored in the identification terminal device in advance, or may be a face image obtained from an official network system according to a user identification card number corresponding to the user wallet account.
According to the embodiment of the application, when the face distribution of the face image is normal, the face image to be identified is sent to the identification terminal equipment, and the identification terminal equipment performs user identity authentication and face payment based on the face image to be identified, so that the success rate of performing user identity authentication and face payment by adopting the face image is ensured.
The method of the embodiments of the present application will be further described with reference to specific examples.
Fig. 2 shows a flowchart of the method for assisting face recognition provided by the embodiment of the application in an actual application scenario, and fig. 3 shows a system block diagram of the method for assisting face recognition provided by the embodiment of the application in an actual application scenario;
Exemplary, the face recognition of the user logs into the wallet account for face payment, as shown in fig. 2 and 3:
At 200, the user is prompted on the terminal device 1 to enter a user handset number. After the user inputs the mobile phone number on the terminal device 1, the terminal device 1 sends the mobile phone number of the user to the identification terminal device.
At 210, the identification terminal device 2 receives the user mobile phone number, searches the user wallet account based on the user mobile phone number, if so, executes step 230, otherwise, executes step 220.
At 220, the identification terminal device 2 prompts the user for new user registration.
At 230, the terminal device 1 acquires a face image.
At 240, the terminal device 1 acquires a face image to be recognized.
At 250, the terminal device 1 obtains the face distribution in the face image, and determines whether the face distribution in the face image is abnormal, and in an exemplary case, whether a face exists in the face image, if so, step 260 is executed, and if not, step 251 is executed.
The terminal device 1 obtains the face distribution in the face image, and determines whether the face distribution in the face image is abnormal, and for example, whether the face image exists in the face image, the specific implementation can participate in the related content in the above embodiment, and the embodiments of the present application are not repeated.
At 251, the terminal device 1 performs a reminder operation corresponding to the face distribution, illustratively, a reminder user located in an image acquisition area of an image acquisition apparatus of the terminal device.
At 260, the terminal device 1 obtains the face distribution in the face image, and determines whether the face distribution in the face image is abnormal, and in an exemplary case, whether there are at least two faces in the face image, if so, step 270 is performed.
At 270, the terminal device 1 obtains the face distribution in the face image, and determines whether the face distribution in the face image is abnormal, and in an exemplary case, whether there are at least two faces with the largest size in the face image, if not, step 280 is executed, and if so, step 271 is executed.
It should be understood that the presence of at least two largest faces in the face image means that the sizes of the at least two largest faces present in the face image are similar.
At 271, the terminal device 1 performs a reminder operation corresponding to the face distribution, and illustratively, the reminder user ensures that no other user is present in the acquisition area of the image acquisition apparatus of the terminal device.
In 280, the terminal device 1 selects the face with the largest size in the face image, determines whether the feature information of the face with the largest size meets the face recognition comparison condition, if not, executes the step 281, otherwise, executes the step 290.
At 281, the terminal device 1 performs a reminding operation corresponding to the face distribution, and illustratively, reminds the user to keep a predetermined distance from the image pickup apparatus of the terminal device.
At 290, comparing the face image with a pre-stored face image;
at 291, the user's identity is authenticated and payment is made from the wallet.
According to the embodiment of the application, the face distribution in the face image is obtained, and the reminding operation corresponding to the face distribution is executed based on the face distribution in the face image when the face distribution is abnormal, so that a user can adjust according to the reminding to remove interference factors, and the accuracy of the face image selection of the user is improved.
The embodiment of the application provides a face recognition method and terminal equipment, which are used for prompting a guiding user to remove interference factors, so that the accuracy of face image selection of the user is improved. The embodiment of the application provides a face recognition method, and an execution subject of the method can be, but is not limited to, terminal equipment or a device or a system capable of being configured to execute the method provided by the embodiment of the application.
For convenience of description, hereinafter, embodiments of the method will be described taking an execution body of the method as an example of a terminal device capable of executing the method. It will be appreciated that the subject of execution of the method is a terminal device which is merely an exemplary illustration and should not be construed as limiting the method.
Fig. 4 is a flowchart of a face recognition method provided by an embodiment of the present application, where the method of fig. 4 may be performed by a terminal device, as shown in fig. 4, and the method may include:
step 410, an image including a plurality of faces is acquired.
The implementation of acquiring the image including the plurality of faces may be acquiring the image including the plurality of faces by scanning or acquiring the image including the plurality of faces by photographing. The embodiment of the present application is not particularly limited.
Step 420, selecting at least one face image from the plurality of face images.
The selection criteria for selecting at least one face image from the plurality of face images may be set according to actual requirements, and the embodiment of the present application is not particularly limited.
Step 430, comparing the face image with the face image of the target user based on the at least one face image.
Step 440, determining whether the identification is successful based on the comparison result.
According to the embodiment of the application, the images comprising the plurality of faces are collected, at least one face image is selected from the images of the plurality of faces, the at least one face image is compared with the face image of the target user, whether the identification is successful or not is determined based on the comparison result, so that whether the face image which can be successfully identified exists in the images comprising the plurality of faces is identified, the success rate of user identity authentication and face payment by adopting the face image is ensured, the whole face payment process can be successfully completed by the subsequent user, and the full-link passing rate is improved. Meanwhile, the method is a process for helping the user learn to use the face payment, so that the user can feel the intelligence of the face payment and is beneficial to the popularization of the face payment due to the unique user experience.
Alternatively, as an embodiment, step 410 may be specifically implemented as:
and acquiring a plurality of faces simultaneously to acquire images including the plurality of faces.
Illustratively, when a plurality of users are located within an image acquisition area of an image acquisition apparatus of a terminal device, faces of the plurality of users are simultaneously acquired.
Alternatively, as an embodiment, step 430 may be specifically implemented as:
And sending the identification of the target user and the characteristic information of the at least one face image to a server so as to compare the face characteristic information of the target user existing on the server through the server.
It should be understood that the server compares the feature information of the at least one face image with the face feature information corresponding to the identification of the target user.
The method comprises the steps of obtaining feature information of at least one face image and pre-stored face feature information of a target user, comparing the two pieces of information, and determining a similarity value of the at least one face image and the pre-stored face image of the target user based on similar features in the two pieces of information. The pre-stored face image of the target user may be a face image corresponding to the user wallet account pre-stored in the identification terminal device, or may be a face image obtained from an official network system according to a user identification card number corresponding to the user wallet account.
According to the embodiment of the application, the identification of the target user and the characteristic information of at least one face image are sent to the server, so that the face characteristic information of the target user on the server is compared through the server, and the identification terminal equipment performs user identity authentication and face payment based on the characteristic information of the at least one face image, thereby ensuring the success rate of performing user identity authentication and face payment by adopting the face image.
Alternatively, as an embodiment, step 420 may be specifically implemented as:
And if the face with the largest size exists in the images of the faces, selecting the face image with the largest size.
For example, if the user stands in the image acquisition area of the image acquisition device of the terminal device with other users (such as friends, relatives or strangers) and back and forth, there are at least two faces in the face image acquired by the image acquisition device. The at least two faces are different in size due to different distances from the image acquisition device. At this time, since the user is located at the nearest position of the image capturing device in actual operation, the size of the face image of the user is usually the largest.
The face with the largest size is the face with the largest weighted size, and the face with the largest weighted size is obtained based on the size of a face detection frame and the distance from the center of the face detection frame to the center of a screen of the terminal equipment. It should be understood that, based on the size of the face detection frame, the result of the face detection frame center to the screen center distance of the terminal device after the face detection frame center to the screen center distance is inversely weighted (i.e., the smaller the face detection frame center to the screen center distance is weighted the greater the distance is weighted the smaller the distance), and the mathematical expression is that the weighted face size = the face detection frame size/the face detection frame center to the screen center distance.
According to the embodiment of the application, the largest-sized face exists in the images of the plurality of faces, so that a user can be encouraged to be positioned at the appointed position of the image acquisition area of the image acquisition device when lose face is performed, and the subsequent processing is facilitated.
Optionally, as an embodiment, before executing step 420, the face recognition method provided by the embodiment of the present application further includes:
And if at least two face images exist in the images of the plurality of faces and the difference between the sizes of the two face images with the largest size in the at least two face images is smaller than a preset threshold value, executing reminding operation.
For example, if the user stands in the image acquisition area of the image acquisition device of the terminal device side by side with other users (such as friends, relatives or strangers), there are at least two faces in the face image acquired by the image acquisition device, and the two faces with the largest size are close in size.
For example, the reminding operation may be an operation for reminding the user to ensure that no other user exists in the acquisition area of the image acquisition device of the terminal device, for example, remind the user to "please ensure that there are no other people around you when brushing the face in order to ensure payment security".
According to the embodiment of the application, when at least two face images exist in the face images and the sizes of the two faces with the largest size are close, reminding operation is performed to remind other users around the user, and the user is guided to clear the other users around the user, so that the accuracy of the acquired face images is ensured, and the payment safety of lose face is ensured.
The above-mentioned details of the face recognition assistance method according to the embodiment of the present application are described with reference to fig. 1 to 3, and the following details of the terminal device according to the embodiment of the present application are described with reference to fig. 5.
Fig. 5 shows a schematic structural diagram of a terminal device according to an embodiment of the present application, and as shown in fig. 5, the terminal device 500 may include:
a first obtaining module 510, configured to obtain a face image to be identified;
a second obtaining module 520, configured to obtain a face distribution in the face image;
And an execution module 530, configured to execute, when the face distribution is abnormal, a reminder operation corresponding to the face distribution based on the face distribution in the face image.
In one embodiment, the second obtaining module 520 includes:
The input unit is used for taking the face image as the input of the multi-face detection model so as to obtain the output face distribution;
the multi-face detection model is obtained by training based on face image samples with abnormal face distribution.
In one embodiment, the abnormal face distribution comprises at least one of the absence of a face in the face image, the presence of at least two faces in the face image, the presence of at least two largest-sized faces in the face image, and the fact that characteristic information of the faces in the face image does not meet a predetermined condition.
In one embodiment, if the face distribution anomaly includes that no face exists in the face image, the executing module 530 includes:
The first reminding unit is used for reminding the user to be positioned in the image acquisition area of the image acquisition device of the terminal equipment based on the fact that no face exists in the face image.
In one embodiment, if the face distribution anomaly includes at least two faces in the face image, the executing module 530 includes:
A first determining unit, configured to determine whether at least two faces exist in the face image, and a difference between sizes of two faces with a largest size is smaller than a predetermined threshold;
And the second reminding unit is used for reminding the user to ensure that no other user exists in the acquisition area of the image acquisition device of the terminal equipment if the first determining unit determines that at least two faces exist in the face image and the difference between the sizes of the two faces with the largest size is smaller than the preset threshold value.
In one embodiment, if the face distribution anomaly includes at least two faces in the face image, the executing module 530 includes:
the selecting unit is used for selecting the face with the largest size in the face image;
the second determining unit is used for determining whether the feature information of the face with the largest size meets the face recognition comparison condition;
And the third reminding unit is used for reminding the user of keeping a preset distance with the image acquisition device of the terminal equipment if the second determining unit determines that the characteristic information of the face with the largest size does not meet the face recognition comparison condition.
In one embodiment, the face with the largest size is a face with the largest weighted size, and the face with the largest weighted size is obtained based on the size of the face detection frame and the distance from the center of the face detection frame to the center of the screen of the terminal device.
According to the embodiment of the application, the face distribution in the face image is obtained, and the reminding operation corresponding to the face distribution is executed based on the face distribution in the face image when the face distribution is abnormal, so that a user can adjust according to the reminding to remove interference factors, and the accuracy of the face image selection of the user is improved.
The face recognition method according to the embodiment of the present application is described in detail above with reference to fig. 4, and the terminal device according to the embodiment of the present application is described in detail below with reference to fig. 6.
Fig. 6 shows a schematic structural diagram of a terminal device according to an embodiment of the present application, and as shown in fig. 6, the terminal device 600 may include:
An acquisition module 610 for acquiring an image including a plurality of faces;
a selection module 620, configured to select at least one face image from the images of the faces;
A comparing module 630, configured to compare the at least one face image with a face image of the target user;
a determining module 640, configured to determine whether the identification is successful based on the comparison result.
According to the embodiment of the application, the images comprising the plurality of faces are collected, at least one face image is selected from the images of the plurality of faces, the at least one face image is compared with the face image of the target user, whether the identification is successful or not is determined based on the comparison result, so that whether the face image which can be successfully identified exists in the images comprising the plurality of faces is identified, the success rate of user identity authentication and face payment by adopting the face image is ensured, the whole face payment process can be successfully completed by the subsequent user, and the full-link passing rate is improved.
The scheme of the embodiment of the application is also a process for helping the user learn to use the face payment, so that the user can feel the intelligence of the face payment and is beneficial to the popularization of the face payment because of the unique user experience.
In one embodiment, the acquisition module 610 includes:
The system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for simultaneously acquiring a plurality of faces so as to acquire images comprising the faces.
In one embodiment, the comparison module 630 includes:
and the sending unit is used for sending the identification of the target user and the characteristic information of the at least one face image to a server so as to compare the face characteristic information of the target user existing on the server through the server.
In one embodiment, the selection module 620 includes:
and the selection unit is used for selecting the face image with the largest size if the face with the largest size exists in the images of the plurality of faces.
In one embodiment, the terminal device 600 further comprises:
and the execution module 650 is configured to execute a reminding operation if at least two face images exist in the images of the faces, and a difference between sizes of two largest face images in the at least two face images is smaller than a predetermined threshold.
Fig. 7 is a schematic structural diagram of a terminal device according to an embodiment of the present disclosure. Referring to fig. 7, at the hardware level, the terminal device includes a processor, and optionally an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory), such as at least 1 disk Memory. Of course, the terminal device may also include hardware required for other services.
The processor, network interface, and memory may be interconnected by an internal bus, which may be an ISA (Industry Standard Architecture ) bus, a PCI (PERIPHERAL COMPONENT INTERCONNECT, peripheral component interconnect standard) bus, or EISA (Extended Industry Standard Architecture ) bus, among others. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 7, but not only one bus or type of bus.
And the memory is used for storing programs. In particular, the program may include program code including computer-operating instructions. The memory may include memory and non-volatile storage and provide instructions and data to the processor.
The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form a device for associating the resource value-added object with the resource object on a logic level. The processor is used for executing the programs stored in the memory and is specifically used for executing the following operations:
acquiring a face image to be recognized;
Acquiring face distribution in the face image;
and when the face distribution is abnormal, executing reminding operation corresponding to the face distribution based on the face distribution in the face image.
According to the embodiment of the application, the face distribution in the face image is obtained, and the reminding operation corresponding to the face distribution is executed based on the face distribution in the face image when the face distribution is abnormal, so that a user can adjust according to the reminding to remove interference factors, and the accuracy of the face image selection of the user is improved.
The above-described face recognition assisting method disclosed in the embodiment shown in fig. 1 of the present specification may be applied to a processor or implemented by the processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software. The Processor may be a general-purpose Processor including a central processing unit (Central Processing Unit, CPU), a network Processor (Network Processor, NP), etc., or may be a digital signal Processor (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components. The various methods, steps, and logic blocks disclosed in one or more embodiments of the present description may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with one or more embodiments of the present disclosure may be embodied directly in a hardware decoding processor or in a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.
The terminal device may also execute the face recognition assisting method executed by the face recognition assisting system of fig. 7, which is not described in detail herein.
Of course, in addition to the software implementation, the terminal device in this specification does not exclude other implementations, such as a logic device or a combination of software and hardware, that is, the execution subject of the following processing flow is not limited to each logic unit, but may also be hardware or a logic device.
Fig. 8 is a schematic structural diagram of a terminal device according to an embodiment of the present disclosure. Referring to fig. 8, at the hardware level, the terminal device includes a processor, and optionally an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory), such as at least 1 disk Memory. Of course, the terminal device may also include hardware required for other services.
The processor, network interface, and memory may be interconnected by an internal bus, which may be an ISA (Industry Standard Architecture ) bus, a PCI (PERIPHERAL COMPONENT INTERCONNECT, peripheral component interconnect standard) bus, or EISA (Extended Industry Standard Architecture ) bus, among others. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 8, but not only one bus or type of bus.
And the memory is used for storing programs. In particular, the program may include program code including computer-operating instructions. The memory may include memory and non-volatile storage and provide instructions and data to the processor.
The processor reads the corresponding computer program from the nonvolatile memory into the memory and then runs the computer program to form a device for associating the resource value-added object with the resource object on a logic level. The processor is used for executing the programs stored in the memory and is specifically used for executing the following operations:
acquiring an image comprising a plurality of faces;
selecting at least one face image from the images of the plurality of faces;
comparing the at least one face image with a face image of the target user;
And determining whether the identification is successful or not based on the comparison result.
According to the embodiment of the application, the images comprising the plurality of faces are collected, at least one face image is selected from the images of the plurality of faces, the at least one face image is compared with the face image of the target user, whether the identification is successful or not is determined based on the comparison result, so that whether the face image which can be successfully identified exists in the images comprising the plurality of faces is identified, the success rate of user identity authentication and face payment by adopting the face image is ensured, the whole face payment process can be successfully completed by the subsequent user, and the full-link passing rate is improved. Meanwhile, the method is a process for helping the user learn to use the face payment, so that the user can feel the intelligence of the face payment and is beneficial to the popularization of the face payment due to the unique user experience.
The face recognition method disclosed in the embodiment shown in fig. 4 of the present specification can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or by instructions in the form of software. The Processor may be a general-purpose Processor including a central processing unit (Central Processing Unit, CPU), a network Processor (Network Processor, NP), etc., or may be a digital signal Processor (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components. The various methods, steps, and logic blocks disclosed in one or more embodiments of the present description may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with one or more embodiments of the present disclosure may be embodied directly in a hardware decoding processor or in a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory, and the processor reads the information in the memory and, in combination with its hardware, performs the steps of the above method.
The terminal device may also perform the face recognition method performed by the face recognition system of fig. 8, which is not described herein.
Of course, in addition to the software implementation, the terminal device in this specification does not exclude other implementations, such as a logic device or a combination of software and hardware, that is, the execution subject of the following processing flow is not limited to each logic unit, but may also be hardware or a logic device.
The embodiments of the present disclosure further provide a computer readable storage medium, where a computer program is stored, where the computer program when executed by a processor implements each process of each method embodiment described above, and the same technical effects can be achieved, and for avoiding repetition, a detailed description is omitted herein. The computer readable storage medium is, for example, a Read-Only Memory (ROM), a random access Memory (Random Access Memory RAM), a magnetic disk or an optical disk.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
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