CN110069965A - A kind of robot personal identification method - Google Patents
A kind of robot personal identification method Download PDFInfo
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- CN110069965A CN110069965A CN201810069531.4A CN201810069531A CN110069965A CN 110069965 A CN110069965 A CN 110069965A CN 201810069531 A CN201810069531 A CN 201810069531A CN 110069965 A CN110069965 A CN 110069965A
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- 238000000034 method Methods 0.000 title claims abstract description 39
- 230000001815 facial effect Effects 0.000 claims abstract description 46
- 230000011218 segmentation Effects 0.000 claims abstract description 22
- 238000001931 thermography Methods 0.000 claims abstract description 5
- 238000012545 processing Methods 0.000 claims description 5
- 230000002708 enhancing effect Effects 0.000 claims description 3
- 230000001537 neural effect Effects 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 239000000284 extract Substances 0.000 claims 1
- 210000004027 cell Anatomy 0.000 description 10
- 238000005516 engineering process Methods 0.000 description 6
- 238000011160 research Methods 0.000 description 6
- 230000000694 effects Effects 0.000 description 3
- 210000000170 cell membrane Anatomy 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 210000003722 extracellular fluid Anatomy 0.000 description 2
- 239000007788 liquid Substances 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 210000001519 tissue Anatomy 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000003909 pattern recognition Methods 0.000 description 1
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- 238000005728 strengthening Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/12—Fingerprints or palmprints
- G06V40/1365—Matching; Classification
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/168—Feature extraction; Face representation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/172—Classification, e.g. identification
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/20—Movements or behaviour, e.g. gesture recognition
- G06V40/23—Recognition of whole body movements, e.g. for sport training
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Abstract
The present invention relates to a kind of robot personal identification methods, the robot personal identification method is the following steps are included: track dynamic subscriber by infrared thermal imaging technique, and according to the paces size of dynamic subscriber, height information, walk velocity estimated dynamic subscriber whether be target user, the facial image of camera acquisition user, facial image is split using binary segmentation method, image enhancement is carried out to the facial image after segmentation, the gray feature point in facial image is obtained, acquired gray feature point is compared with default gray feature point.Robot personal identification method provided by the invention, head can accurately obtain the feature of face, accurately, safely identify the identity of user, while on the basis of recognition of face, the impedance operator for acquiring the palm cell of user, substantially increases the accuracy and safety of identification.
Description
Technical field
The present invention relates to robotic technology field more particularly to a kind of robot personal identification methods.
Background technique
In recent years, computer image technology will be used wider and wider, and utilize computer, image processing, pattern-recognition
Etc. technologies realize authentication also increasingly become present mode identification and artificial intelligence field a research hotspot, face know
It is not mainly used in the sides such as public security (criminal's identification etc.), entry and exit verifying, airport security, security authentication systems, credit card validation
Face.Face identification system as an advanced high-tech technological prevention and verifying means, in some economically developed countries and
Area has been widely used for the high security requirement such as scientific research, industry, museum, hotel, market, medical monitoring, bank, prison
Important place, have broad application prospects.
Since biological characteristic is the inherent attribute of people, there is very strong self stability and individual difference, therefore be body
The most ideal foundation of part verifying.The human bodies such as fingerprint, palmmprint, eye iris, DNA (DNA) and the face appearance of people are special
Levy have human body intrinsic irreproducible uniqueness, stability, can not replicate, it is stolen or pass into silence.Due to everyone
These features are different from, therefore everyone identity can be accurately identified using these unique physiological characteristics of human body,
Current existing human-body biological recognition methods includes recognition of face, fingerprint recognition, voice recognition, the palm shape identification, signature recognition, eye
Iris, retina identification etc..Wherein, carrying out authentication using face characteristic is most naturally direct means again, compared to other
Human body biological characteristics, it has the characteristics that direct, friendly, convenient, it is easier to be received by user, therefore is concerned.
Embedded human face intelligent recognition relates generally to camera calibration, object identification, motion segmentation and tracking, image data
The contents such as processing, high-level semantic understanding, are the forward position research directions of computer vision field.It is with a wide range of applications and
Huge potential age deduction has caused many scientific research institutions and the great interest of researcher.For example, British scientist is just
Identify new technology in exploitation " intelligence ", this technology be expected to make following closed-circuit television monitor not only can automatic identification pickpocket and
Cartheft, but also can forecast the robbery with violence that may occur in subway or airport or terrorist activity；H.J.Zhang etc. is proposed
Based on the intelligent monitoring shot segmentation algorithm of interframe histogram difference, because its algorithm complexity is low, shot segmentation effect is good, becomes
Method popular at present；At home, Institute of Automation Research of CAS, Tsinghua University and the Chinese Academy of Sciences calculate institute
Etc. all strengthening relevant research.
Embedded human face intelligent identifying system is directly hidden with face acquisition, face characteristic information amount of coded data is small,
The advantages that recognition speed is fast, recognition accuracy is high, reject rate is low, examination is easy, highly-safe, use condition is simple is a kind of
Directly, conveniently, be easy the non-infringement identity identifying method being accepted.
Therefore, accuracy, the safety for how improving recognition of face become those skilled in the art's technology urgently to be resolved
Problem.
Summary of the invention
The object of the present invention is to provide a kind of robot personal identification methods, can be improved the accuracy and peace of recognition of face
Quan Xing.
To achieve the goals above, the present invention provides a kind of robot personal identification method, the robot identity is known
Other method the following steps are included:
Step S01, basic user data library is established, includes the essential information of elemental user in the database；
Step S02, by infrared thermal imaging technique track dynamic subscriber, and according to the paces size of dynamic subscriber, height information,
Whether velocity estimated dynamic subscriber is target user on foot, if target user then enters step S03, if not target user is then
Step S02 is repeated,
Step S03, in background image, camera obtain user facial image, using binary segmentation method to facial image into
Row segmentation；
Step S04, image enhancement is carried out to the facial image after segmentation, obtains the gray feature point in facial image, will be obtained
The gray feature point taken is compared with default gray feature point, if acquired gray feature point and default gray feature point are not
Unanimously, then user identity identification is issued unsuccessfully to prompt, if acquired gray feature point is consistent with default gray feature point, into
Enter step S05；
Step S05, on the basis of obtaining the facial image of user, the external appearance characteristic amount comprising user's face is extracted, by face
External appearance characteristic amount checked with preset external appearance characteristic amount, and judge face similarity, if similarity is greater than preset value,
S06 is entered step, if similarity is less than preset value, user identity identification is issued and unsuccessfully prompts；
Step S06, the palm of user is put on electrical impedance acquisition device, the electrical impedance acquisition device acquisition predeterminated frequency
Exciting current flows through impedance operator when user's palm cell, if the impedance operator is consistent with default impedance operator, basis
Default impedance operator judges the identity of the user corresponding to it, if the impedance operator and default impedance operator are inconsistent,
User identity identification is issued unsuccessfully to prompt.
Preferably, the predeterminated frequency of the exciting current is 65KHz.
Preferably, in step S02, judge whether dynamic subscriber is target user, is judged according to the following steps:
S021, whether the height information of collected dynamic subscriber is fallen elemental user in the database height altitude range it
It is interior, if it is, into S022；
S022, whether the paces size of collected dynamic subscriber is fallen elemental user in the database paces size range it
It is interior, if it is, into S023；
S023, whether the speed of walking of collected dynamic subscriber is fallen elemental user in the database walk velocity interval it
It is interior, if it is, entering step S03.
Preferably, in step S04, if acquired gray feature point and default gray feature point are inconsistent, use is issued
After family identification unsuccessfully prompts, further includes:
The fingerprint of user is placed on fingerprint identification device, the finger print information of the fingerprint identification device identification user, and will
The finger print information of the identification is compared with preset fingerprint information, if the finger print information of the identification and preset fingerprint information one
It causes, then enters step S05, the finger print information and preset fingerprint information of the identification are inconsistent, then issue user identity identification mistake
Lose prompt.
Preferably, in step S04, the facial image after described pair of segmentation carries out image enhancement, comprising: to the people after segmentation
Face image carries out histogram enhancement, compares enhancing to the facial image after histogram enhancement, to the gray scale of facial image into
Row segment processing.
Preferably, described that facial image is split using binary segmentation method, comprising: to extract the people in step S03
Facial image is separated into the face with different grey-scale by the difference in face image between facial contour feature and background image
Pixel in facial image is compared by contour area and background area with threshold value, if the pixel and the threshold value
Unanimously, then judge the pixel for face contour area.
Preferably, the robot further includes voice command reception device, if judging the identity of user, the voice life
It enables reception device receive the voice command of user, and executes corresponding operation according to institute's speech commands.
Preferably, in step S06, the resistance characteristic is that nonlinear electrical impedance is general, is obtained by network neural training
The default impedance operator of user.
Robot personal identification method provided by the invention tracks dynamic subscriber, judgement by outer thermal imaging first
Whether dynamic subscriber is target user, on the basis of judging target user, by extracting the gray feature value in facial image
Aspect ratio as recognition of face can accurately obtain the feature of face, accurately, safely identify the body of user to object
Part, while on the basis of recognition of face, the impedance operator of the palm cell of user is acquired, according to everyone palm cell
Impedance characteristic is different, further identifies to the identity characteristic of user, substantially increases the accuracy and peace of identification
Quan Xing.
Detailed description of the invention
Fig. 1 is a kind of flow diagram of specific embodiment of robot personal identification method provided by the invention.
Specific embodiment
Below in conjunction with the attached drawing in the embodiment of the present application, technical solutions in the embodiments of the present application carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of embodiments of the present application, instead of all the embodiments.It is based on
Embodiment in the application, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall in the protection scope of this application.
Fig. 1 is please referred to, Fig. 1 is a kind of process of specific embodiment of robot personal identification method provided by the invention
Schematic diagram.
As shown in Figure 1, the present invention provides a kind of robot personal identification method, the robot personal identification method packet
Include following steps:
Step S01, basic user data library is established, includes the essential information of elemental user in the database；
Step S02, by infrared thermal imaging technique track dynamic subscriber, and according to the paces size of dynamic subscriber, height information,
Whether velocity estimated dynamic subscriber is target user on foot, if target user then enters step S03, if not target user is then
Step S02 is repeated,
Step S03, in background image, camera obtain user facial image, using binary segmentation method to facial image into
Row segmentation；
Step S04, image enhancement is carried out to the facial image after segmentation, obtains the gray feature point in facial image, will be obtained
The gray feature point taken is compared with default gray feature point, if acquired gray feature point and default gray feature point are not
Unanimously, then user identity identification is issued unsuccessfully to prompt, if acquired gray feature point is consistent with default gray feature point, into
Enter step S05；
Step S05, on the basis of obtaining the facial image of user, the external appearance characteristic amount comprising user's face is extracted, by face
External appearance characteristic amount checked with preset external appearance characteristic amount, and judge face similarity, if similarity is greater than preset value,
S06 is entered step, if similarity is less than preset value, user identity identification is issued and unsuccessfully prompts；
Step S06, the palm of user is put on electrical impedance acquisition device, the electrical impedance acquisition device acquisition predeterminated frequency
Exciting current flows through impedance operator when user's palm cell, if the impedance operator is consistent with default impedance operator, basis
Default impedance operator judges the identity of the user corresponding to it, if the impedance operator and default impedance operator are inconsistent,
User identity identification is issued unsuccessfully to prompt.
Bio-electrical impedance is that a kind of electric signal passes through the impedance operator reflected when living organism, and biological tissue includes cell
Interior liquid, extracellular fluid and cell membrane, when the electric signal of certain frequency is applied to organism, typical case will bypass cell, main to flow
Through extracellular fluid, when being applied to the frequency increase of biological tissue's electric signal, one part of current passes through cell membrane and flows through cell
Interior liquid.The bio-electrical impedance information of different people has uniqueness, and bio-electrical impedance information can be used to carry out identification.
Robot personal identification method provided by the invention, by extracting the gray feature value in facial image as face
The aspect ratio of identification can accurately obtain the feature of face, accurately, safely identify the identity of user, simultaneously to object
On the basis of recognition of face, the impedance operator of the palm cell of user is acquired, it is special according to the impedance of everyone palm cell
Sign is different, further identifies to the identity characteristic of user, substantially increases the accuracy and safety of identification.
In preferred scheme, in step S02, judge whether dynamic subscriber is target user, is judged according to the following steps:
S021, whether the height information of collected dynamic subscriber is fallen elemental user in the database height altitude range it
It is interior, if it is, into S022；
S022, whether the paces size of collected dynamic subscriber is fallen elemental user in the database paces size range it
It is interior, if it is, into S023；
S023, whether the speed of walking of collected dynamic subscriber is fallen elemental user in the database walk velocity interval it
It is interior, if it is, entering step S03.
In preferred scheme, the predeterminated frequency of the exciting current is 65KHz, when the frequency of exciting current is 65KHz, is swashed
Encourage electric current to human body be accordingly it is best, can more accurately obtain impedance operator.
In preferred scheme, if acquired gray feature point and default gray feature point are inconsistent, user's body is issued
It further include that the fingerprint of user is placed on fingerprint identification device, the fingerprint identification device is known after part recognition failures prompt
The finger print information of other user, and the finger print information of the identification is compared with preset fingerprint information, if the finger of the identification
Line information is consistent with preset fingerprint information, then flows through user hand by the exciting current that electrical impedance acquisition device acquires predeterminated frequency
Impedance operator when cell is slapped, the finger print information and preset fingerprint information of the identification are inconsistent, then issue user identity identification
Failure prompts.
There is the error identified in recognition of face in order to prevent, after recognition of face failure, passes through fingerprint recognition user identity
Information, if after user is identified by finger print information, then electrical impedance identification is carried out, improve the accuracy of identification.
In preferred scheme, the facial image after described pair of segmentation carries out image enhancement, comprising: to the face figure after segmentation
As carrying out histogram enhancement, enhancing is compared to the facial image after histogram enhancement, the gray scale of facial image is divided
Section processing.The histogram of image is important statistical nature, it can be the approximation of image gray-scale level density function, has corresponding
Statistical nature.
It is described that facial image is split using binary segmentation method, comprising: to extract the face figure in preferred scheme
Difference as between facial contour feature and background image, is separated into the facial contour with different grey-scale for facial image
Pixel in facial image is compared by region and background area with threshold value, if the pixel is consistent with the threshold value,
Then judge the pixel for face contour area.
In preferred scheme, the robot further includes voice command reception device, described if judging the identity of user
Voice command reception device receives the voice command of user, and corresponding operation is executed according to institute's speech commands.Pass through body
The user of part identification can pass through speech command operation robot.
In preferred scheme, the resistance characteristic is that nonlinear electrical impedance is general, obtains user by network neural training
Default impedance operator.
Structure, feature and effect of the invention, the above institute are described in detail based on the embodiments shown in the drawings
Only presently preferred embodiments of the present invention is stated, but the present invention does not limit the scope of implementation as shown in the drawings, it is all according to structure of the invention
Think made change or equivalent example modified to equivalent change, when not going beyond the spirit of the description and the drawings,
It should all be within the scope of the present invention.
Claims (8)
1. a kind of robot personal identification method, which is characterized in that the robot personal identification method the following steps are included:
Step S01, basic user data library is established, includes the essential information of elemental user in the database；
Step S02, by infrared thermal imaging technique track dynamic subscriber, and according to the paces size of dynamic subscriber, height information,
Whether velocity estimated dynamic subscriber is target user on foot, if target user then enters step S03, if not target user is then
Step S02 is repeated,
Step S03, in background image, camera obtain user facial image, using binary segmentation method to facial image into
Row segmentation；
Step S04, image enhancement is carried out to the facial image after segmentation, obtains the gray feature point in facial image, will be obtained
The gray feature point taken is compared with default gray feature point, if acquired gray feature point and default gray feature point are not
Unanimously, then user identity identification is issued unsuccessfully to prompt, if acquired gray feature point is consistent with default gray feature point, into
Enter step S05；
Step S05, on the basis of obtaining the facial image of user, the external appearance characteristic amount comprising user's face is extracted, by face
External appearance characteristic amount checked with preset external appearance characteristic amount, and judge face similarity, if similarity is greater than preset value,
S06 is entered step, if similarity is less than preset value, user identity identification is issued and unsuccessfully prompts；
Step S06, the palm of user is put on electrical impedance acquisition device, the electrical impedance acquisition device acquisition predeterminated frequency
Exciting current flows through impedance operator when user's palm cell, if the impedance operator is consistent with default impedance operator, basis
Default impedance operator judges the identity of the user corresponding to it, if the impedance operator and default impedance operator are inconsistent,
User identity identification is issued unsuccessfully to prompt.
2. robot personal identification method according to claim 1, which is characterized in that the predeterminated frequency of the exciting current
For 65KHz.
3. robot personal identification method according to claim 1, which is characterized in that in step S02, judge dynamic subscriber
Whether it is target user, is judged according to the following steps:
S021, whether the height information of collected dynamic subscriber is fallen elemental user in the database height altitude range it
It is interior, if it is, into S022；
S022, whether the paces size of collected dynamic subscriber is fallen elemental user in the database paces size range it
It is interior, if it is, into S023；
S023, whether the speed of walking of collected dynamic subscriber is fallen elemental user in the database walk velocity interval it
It is interior, if it is, entering step S03.
4. robot personal identification method according to claim 1, which is characterized in that in step S04, if acquired ash
It spends characteristic point and default gray feature point is inconsistent, then issue after user identity identification unsuccessfully prompts, further includes:
The fingerprint of user is placed on fingerprint identification device, the finger print information of the fingerprint identification device identification user, and will
The finger print information of the identification is compared with preset fingerprint information, if the finger print information of the identification and preset fingerprint information one
It causes, then enters step S05, the finger print information and preset fingerprint information of the identification are inconsistent, then issue user identity identification mistake
Lose prompt.
5. robot personal identification method according to claim 3, which is characterized in that in step S04, after described pair of segmentation
Facial image carry out image enhancement, comprising: to after segmentation facial image carry out histogram enhancement, after histogram enhancement
Facial image compares enhancing, carries out segment processing to the gray scale of facial image.
6. robot personal identification method according to claim 4, which is characterized in that described to use two-value in step S03
Split plot design is split facial image, comprising: extracts in the facial image between facial contour feature and background image
Facial image is separated into facial contour region and background area with different grey-scale, by the picture in facial image by difference
Vegetarian refreshments is compared with threshold value, if the pixel is consistent with the threshold value, judges the pixel for face contour area.
7. robot personal identification method according to claim 1, which is characterized in that the robot further includes voice life
Reception device is enabled, if judging the identity of user, institute's speech commands reception device receives the voice command of user, and according to institute
Speech commands execute corresponding operation.
8. robot personal identification method according to claim 1, which is characterized in that in step S06, the resistance characteristic
It is general for nonlinear electrical impedance, the default impedance operator of user is obtained by network neural training.
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| CN201810069531.4A CN110069965A (en) | 2018-01-24 | 2018-01-24 | A kind of robot personal identification method |
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Cited By (6)
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| CN111931714A (en) * | 2020-09-21 | 2020-11-13 | 德鲁动力科技(海南)有限公司 | Visual perception method and system |
| CN112788238A (en) * | 2021-01-05 | 2021-05-11 | 中国工商银行股份有限公司 | Control method and device for robot following |
| CN112949495A (en) * | 2021-03-04 | 2021-06-11 | 安徽师范大学 | Intelligent identification system based on big data |
| CN115145163A (en) * | 2022-06-30 | 2022-10-04 | 嘉兴慕思寝室用品有限公司 | Intelligent control method, intelligent carpet, electronic equipment and storage medium |
| WO2023025219A1 (en) * | 2021-08-27 | 2023-03-02 | 上海微创医疗机器人（集团）股份有限公司 | Method, device and system for identifying operator of medical robot |
| CN116959075A (en) * | 2023-08-01 | 2023-10-27 | 湖北省电子信息产品质量监督检验院 | Deep learning-based iterative optimization method for identity recognition robot |
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