CN112990019A - Face recognition living body detection method - Google Patents
Face recognition living body detection method Download PDFInfo
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- CN112990019A CN112990019A CN202110290870.7A CN202110290870A CN112990019A CN 112990019 A CN112990019 A CN 112990019A CN 202110290870 A CN202110290870 A CN 202110290870A CN 112990019 A CN112990019 A CN 112990019A
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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
- G06V40/161—Detection; Localisation; Normalisation
- G06V40/166—Detection; Localisation; Normalisation using acquisition arrangements
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
- G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
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- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
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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
- G06V40/174—Facial expression recognition
- G06V40/176—Dynamic expression
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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
- G06V40/40—Spoof detection, e.g. liveness detection
- G06V40/45—Detection of the body part being alive
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Abstract
The invention discloses a face recognition living body detection method, which specifically comprises the following steps: s1, establishing a corresponding detection system and a system module configured by the detection system according to the selected living body detection method; the invention relates to the technical field of face recognition detection. This face identification live body detection method, through letting live body detection system and face identification system's better cooperation use, let personnel face information identification examine time measuring more accurate, changed the live body order that adopts in the past during the live body examination in addition, totally disorderly the information of gathering, adopt random form, the effectual people of avoiding appear repeating or seek its law, let whole detection system safe and reliable more, this people of being convenient for use.
Description
Technical Field
The invention relates to the technical field of face recognition detection, in particular to a face recognition living body detection method.
Background
Face recognition is a biometric technology for identity recognition based on facial feature information of a person. In the face recognition application, the living body detection can verify whether a user is operated by a real living body by combining actions of blinking, mouth opening, head shaking, head nodding and the like, and by using technologies such as face key point positioning, face tracking and the like. Common attack means such as photos, face changing, masks, sheltering and screen copying can be effectively resisted, so that a user is helped to discriminate fraudulent behaviors, and the benefit of the user is guaranteed.
The existing face recognition living body detection method cannot be completely matched with a face recognition system for use, so that the use effect is greatly reduced, the use is very inconvenient for people, the detection effect is correspondingly reduced, the use is not beneficial for people, and in subsequent use optimization, the face recognition living body detection method does not have a self-optimization function, so that the face recognition living body detection method is not convenient for people to use.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a face recognition living body detection method, which solves the problems in the background technology.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme: a face recognition living body detection method specifically comprises the following steps:
s1, establishing a corresponding detection system and a system module configured by the detection system according to the selected living body detection method (the detection method is classified detection aiming at various different crowds, the specific measures are that time-sharing instructions are adopted for detection and identification, after face information is recorded into the system, the main control system randomly distributes an instruction to a user according to the selection of random events, the user makes corresponding action or takes corresponding measures and is recorded by the system again, and at the moment, the user is counted to pass the detection);
s2, the whole detection system and the face recognition system are subjected to negotiation and connection establishment, so that the living body detection system and the face recognition system can be matched for use and mutually have control authority (the highest control authority is set as manual control);
s3, when the face recognition system works, the face data of an external user is input, then face recognition detection is carried out, at the moment, the living body detection system carries out intervention work, interferes with the face recognition system, sends a following action instruction to the user to display, and then the user makes a corresponding action, at the moment, the face recognition system captures the action expression of the face of the user, and compares the action expression with the instruction given by the living body detection system to detect whether the action expression is matched with the instruction given by the living body detection system;
s4, analyzing the recognition mode of the face recognition system, detecting several emphasis points of the face, detecting the recognition blind areas, summarizing and feeding back, transmitting to a controller, and performing repair adjustment to optimize the recognition;
s5, judging the data of the random face action fed back by the face recognition system, judging whether the user makes a correct action and whether the user matches the current face, if not, the user can not pass the verification, marking that the user identity authentication fails, if the user is correctly recognized, the user identity authentication is successful, and displaying the judged result;
and S6, analyzing according to the obtained information data, analyzing according to the recognition speed and the recognition quantity, detecting the step consuming more time and more resources, and optimizing according to the step to continuously ensure the fluency of the system (for example, the human face recognition system is slower in speed when recognizing the human face pupils and occupies more system power, the step is optimized to accelerate the pupil recognition speed).
Preferably, the system modules involved in S1 are: the system comprises a processor, a sorter, a controller, a data comparator, a data analysis module and an information wireless transceiving module, wherein all the system modules are mutually and bidirectionally connected.
Preferably, the system modules involved in S1 are: the system comprises a processor, a sorter, a controller, a data comparator, a data analysis module and an information wireless transceiving module, wherein all the system modules are mutually and bidirectionally connected.
Preferably, the system entry data are stored and recorded, stored in the cloud storage module and the big data storage center, and protected by password encryption.
Preferably, the password protection device has a self-protection function and is involved with a network alarm center, and the password protection device is involved with the network alarm center for warning when the passwords are verified for multiple times and all the passwords are wrong.
Preferably, the user face action capturing adopts a point capturing method, multiple monitoring points are set according to the face characteristics of the user, and the point array wiring harness is identified and detected during the living body detection.
Preferably, the random command in S1 is updated at any time, the command database is encrypted, and both reading and writing require identity verification and password input.
(III) advantageous effects
The invention provides a face recognition living body detection method. The beneficial effects are as follows: this face identification live body detection method, through letting live body detection system and face identification system's better cooperation use, let personnel face information identification examine time measuring more accurate, changed the live body order that adopts in the past during the live body examination in addition, totally disorderly the information of gathering, adopt random form, the effectual people of avoiding appear repeating or seek its law, let whole detection system safe and reliable more, this people of being convenient for use.
Drawings
Fig. 1 is a block diagram of the system principle of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, an embodiment of the present invention provides a technical solution: a face recognition living body detection method specifically comprises the following steps:
s1, establishing a corresponding detection system and a system module configured by the detection system according to the selected living body detection method (the detection method is classified detection aiming at various different crowds, the specific measures are that time-sharing instructions are adopted for detection and identification, after face information is recorded into the system, the main control system randomly distributes an instruction to a user according to the selection of random events, the user makes corresponding action or takes corresponding measures and is recorded by the system again, and at the moment, the user is counted to pass the detection);
s2, the whole detection system and the face recognition system are subjected to negotiation and connection establishment, so that the living body detection system and the face recognition system can be matched for use and mutually have control authority (the highest control authority is set as manual control);
s3, when the face recognition system works, the face data of an external user is input, then face recognition detection is carried out, at the moment, the living body detection system carries out intervention work, interferes with the face recognition system, sends a following action instruction to the user to display, and then the user makes a corresponding action, at the moment, the face recognition system captures the action expression of the face of the user, and compares the action expression with the instruction given by the living body detection system to detect whether the action expression is matched with the instruction given by the living body detection system;
s4, analyzing the recognition mode of the face recognition system, detecting several emphasis points of the face, detecting the recognition blind areas, summarizing and feeding back, transmitting to a controller, and performing repair adjustment to optimize the recognition;
s5, judging the data of the random face action fed back by the face recognition system, judging whether the user makes a correct action and whether the user matches the current face, if not, the user can not pass the verification, marking that the user identity authentication fails, if the user is correctly recognized, the user identity authentication is successful, and displaying the judged result;
and S6, analyzing according to the obtained information data, analyzing according to the recognition speed and the recognition quantity, detecting the step consuming more time and more resources, and optimizing according to the step to continuously ensure the fluency of the system (for example, the human face recognition system is slower in speed when recognizing the human face pupils and occupies more system power, the step is optimized to accelerate the pupil recognition speed).
In the present invention, the system modules involved in S1 are: the system comprises a processor, a sorter, a controller, a data comparator, a data analysis module and an information wireless transceiving module, wherein all the system modules are mutually and bidirectionally connected.
In the present invention, the system modules involved in S1 are: the system comprises a processor, a sorter, a controller, a data comparator, a data analysis module and an information wireless transceiving module, wherein all the system modules are mutually and bidirectionally connected.
In the invention, the system input data are stored and recorded, and are stored in the cloud storage module and the big data storage center and are encrypted and protected by passwords.
In the invention, the password protection device has a self-protection function and is interfered with a network alarm center, and the password is authenticated for many times and is interfered with the network alarm center for warning when the passwords are all wrong.
In the invention, the user face action capture adopts a point capture method, multi-point monitoring points are set according to the user face characteristics, and the point array wiring harness is identified and detected during the living body detection.
In the invention, the random instruction in the S1 is updated at any time, the instruction database is of an encryption property, and the identity input password needs to be verified in both reading and writing.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, 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.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. A face recognition living body detection method specifically comprises the following steps:
s1, establishing a corresponding detection system and a system module configured by the detection system according to the selected living body detection method (the detection method is classified detection aiming at various different crowds, the specific measures are that time-sharing instructions are adopted for detection and identification, after face information is recorded into the system, the main control system randomly distributes an instruction to a user according to the selection of random events, the user makes corresponding action or takes corresponding measures and is recorded by the system again, and at the moment, the user is counted to pass the detection);
s2, the whole detection system and the face recognition system are subjected to negotiation and connection establishment, so that the living body detection system and the face recognition system can be matched for use and mutually have control authority (the highest control authority is set as manual control);
s3, when the face recognition system works, the face data of an external user is input, then face recognition detection is carried out, at the moment, the living body detection system carries out intervention work, interferes with the face recognition system, sends a following action instruction to the user to display, and then the user makes a corresponding action, at the moment, the face recognition system captures the action expression of the face of the user, and compares the action expression with the instruction given by the living body detection system to detect whether the action expression is matched with the instruction given by the living body detection system;
s4, analyzing the recognition mode of the face recognition system, detecting several emphasis points of the face, detecting the recognition blind areas, summarizing and feeding back, transmitting to a controller, and performing repair adjustment to optimize the recognition;
s5, judging the data of the random face action fed back by the face recognition system, judging whether the user makes a correct action and whether the user matches the current face, if not, the user can not pass the verification, marking that the user identity authentication fails, if the user is correctly recognized, the user identity authentication is successful, and displaying the judged result;
and S6, analyzing according to the obtained information data, analyzing according to the recognition speed and the recognition quantity, detecting the step consuming more time and more resources, and optimizing according to the step to continuously ensure the fluency of the system (for example, the human face recognition system is slower in speed when recognizing the human face pupils and occupies more system power, the step is optimized to accelerate the pupil recognition speed).
2. The face recognition live body detection method according to claim 1, characterized in that: the system modules involved in S1 are: the system comprises a processor, a sorter, a controller, a data comparator, a data analysis module and an information wireless transceiving module, wherein all the system modules are mutually and bidirectionally connected.
3. The face recognition live body detection method according to claim 1, characterized in that: the system entry data are stored and recorded, stored in the cloud storage module and the big data storage center and protected by password encryption.
4. The face recognition live body detection method according to claim 3, characterized in that: the password protection device has a self-protection function and is in conflict with the network alarm center, and the password is verified for many times and is in conflict with the network alarm center to warn.
5. The face recognition live body detection method according to claim 1, characterized in that: the user face action capturing adopts a point capturing method, multi-point monitoring points are set according to the user face characteristics, and the point array wiring harness is identified and detected during in vivo detection.
6. The face recognition live body detection method according to claim 1, characterized in that: the random command in the S1 is updated at any time, and the command database is encrypted, and both reading and writing require authentication to input a password.
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| CN202110290870.7A CN112990019A (en) | 2021-03-18 | 2021-03-18 | Face recognition living body detection method |
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| CN202110290870.7A CN112990019A (en) | 2021-03-18 | 2021-03-18 | Face recognition living body detection method |
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102789572A (en) * | 2012-06-26 | 2012-11-21 | 五邑大学 | Living body face safety certification device and living body face safety certification method |
| CN104751110A (en) * | 2013-12-31 | 2015-07-01 | 汉王科技股份有限公司 | Bio-assay detection method and device |
| CN111444831A (en) * | 2020-03-25 | 2020-07-24 | 深圳中科信迅信息技术有限公司 | Method for recognizing human face through living body detection |
| CN111666835A (en) * | 2020-05-20 | 2020-09-15 | 广东志远科技有限公司 | Face living body detection method and device |
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- 2021-03-18 CN CN202110290870.7A patent/CN112990019A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102789572A (en) * | 2012-06-26 | 2012-11-21 | 五邑大学 | Living body face safety certification device and living body face safety certification method |
| CN104751110A (en) * | 2013-12-31 | 2015-07-01 | 汉王科技股份有限公司 | Bio-assay detection method and device |
| CN111444831A (en) * | 2020-03-25 | 2020-07-24 | 深圳中科信迅信息技术有限公司 | Method for recognizing human face through living body detection |
| CN111666835A (en) * | 2020-05-20 | 2020-09-15 | 广东志远科技有限公司 | Face living body detection method and device |
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