CN113505717B - Online passing system based on face and facial feature recognition technology - Google Patents
Online passing system based on face and facial feature recognition technology Download PDFInfo
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Abstract
The invention discloses an online traffic system based on a face feature recognition technology. The system comprises an acquisition module, an extraction module, an initialization module, a calculation module, a matching module, a detection module and an optimization module. A characteristic face matching algorithm is adopted to realize higher-accuracy face recognition; the online/offline living body detection service is adopted to identify whether the user is a real person or not, and cheating tools such as photos, videos and 3D molds are effectively resisted; and the space and time complexity of the system is optimized by adopting a system optimization algorithm, the response time of the system is reduced, and the fluency of the system is improved. The face and face feature recognition system can prevent lawless persons from cheating by using tools such as photos, videos and 3D molds of other persons, can effectively improve face recognition accuracy and traffic efficiency of the existing face recognition system, can be widely applied to the fields of financial industry, retail industry, security traffic and the like, and meets business requirements of identity verification, face attendance, gate traffic and the like.
Description
Technical Field
The invention belongs to the technical field of computer vision and face recognition, and particularly relates to an online passing system based on a face and face feature recognition technology.
Background
The face recognition comprises the aspects of face search, face comparison, living body detection and the like, can be flexibly applied to industry scenes such as finance, retail, security, community face brushing access, campus intelligent access control, enterprise intelligent access control and the like, and meets the business requirements of identity verification, face attendance, gate access and the like. The face searching is to compare the face collected on site with N faces in a designated face library to find one or more most similar faces for user identity recognition and verification; the face comparison is to compare the similarity between the user photo collected on site and the reserved photo of the user, and judge whether the user operates the face comparison, so as to ensure the authenticity of the user identity; the live body detection is online or offline live body detection and is used for identifying whether a user in a service scene is a real person or not and effectively resisting cheating tools such as photos, videos and 3D molds.
Although the current common online traffic systems based on face recognition are mature, the systems still have some disadvantages in the following aspects:
(1) the face recognition accuracy is not high, and misjudgment is easy to occur;
(2) lawbreakers can scan and pass by using cheating tools such as photos, videos and 3D molds of other people;
(3) the system response time is long, the system optimization is insufficient, and the fluency is insufficient.
Therefore, the current face recognition passing system has more problems in the aspects of face recognition accuracy, personal operation, system response time, smoothness optimization and the like, so that the development of a stable, efficient and accurate online passing system becomes a hot problem to be solved urgently in the field of face recognition. The online traffic system based on the face and face feature recognition technology provided by the invention extracts face and face feature points, adopts a feature face matching algorithm, a system optimization algorithm and online/offline living body detection service to reduce the response time of the system, improves the smoothness of the system, can effectively prevent lawless persons from cheating by using other people's photos, videos, 3D molds and other tools, improves the face recognition accuracy and traffic efficiency of the existing face recognition system, can be widely applied to the fields of financial industry, retail industry, security traffic and the like, and meets the business requirements of identity verification, face attendance, gate traffic and the like.
Disclosure of Invention
The invention aims to overcome the defect that the existing face recognition passing system is insufficient in face identification capability, and provides an online passing system based on a face facial feature recognition technology. The system adopts a characteristic face matching algorithm to realize higher-accuracy face recognition; adopting an online/offline living body detection service to identify whether the scanning behavior is personal operation; and the system is optimized by adopting a system optimization algorithm, so that the response time of the system is reduced, and the fluency of the system is improved.
The invention is realized by the following steps: an online passing system based on a face and facial feature recognition technology comprises: the device comprises an acquisition module, an extraction module, an initialization module, a calculation module, a matching module, a detection module and an optimization module. The acquisition module is connected with the acquisition module, the acquisition module is connected with the extraction module, the extraction module is connected with the initialization module, the initialization module is connected with the calculation module, the calculation module is connected with the matching module, the matching module is connected with the detection module, and the detection module is connected with the optimization module.
The acquisition module is used for acquiring a face image to be recognized.
The acquisition module is used for acquiring the feature points in the acquired face image to be recognized, the selected feature points comprise key points and dense points, the key points comprise 5 points which are respectively a left eye center point, a right eye center point, a nose tip, a mouth left boundary point and a mouth right boundary point, the dense points are N points except the key points, and the acquisition is carried out after the coordinates (x, y) of each dense feature point are calculated.
And the extraction module is used for sequentially extracting the feature vectors of the M-N +5 feature points according to the pixel value arrangement of each local area of the face image.
The initialization module is used for initializing and constructing the weights and the projection matrix of the M characteristic points.
The calculation module is configured to calculate weighted collaborative representation of the feature vectors of the M feature points to obtain a representation coefficient of the feature vector of the feature point.
The matching module is used for matching the acquired face image with the existing face database and judging whether the matching is successful or not, and the implementation mainly comprises the following steps:
s1: and reading the weights of M characteristic points in the face image acquired on site, the projection matrix and the representation coefficients of the characteristic vectors of the projection matrix.
S2: traversing the existing face database, and sequentially calculating the weight of each face image feature point in the face database, the projection matrix and the representation coefficient of the feature vector thereof.
S3: and comparing the weights of M characteristic points in the acquired face image, the projection matrix and the expression coefficients of the characteristic vectors thereof with the corresponding parameter values of each face image in the face database by adopting a characteristic face matching algorithm.
S4: and calculating the average similarity of the parameter values of the two human face images.
S5: and judging whether the average similarity of the parameter values is more than or equal to 90 percent.
S6: and if the average similarity is greater than or equal to 90%, the matching is successful, otherwise, the matching is failed.
S7: the matching module ends.
The detection module adopts online/offline living body detection service to identify whether the scanning behavior is personally operated.
The optimization module adopts a system optimization algorithm to optimize the system, so that the response time of the system is shortened, and the fluency of the system is improved.
Compared with the existing face recognition passing system, the online passing system based on the face feature recognition technology has the following advantages:
(1) a characteristic face matching algorithm is adopted to realize higher-accuracy face recognition;
(2) the online/offline living body detection service is adopted to identify whether the user is a real person or not, and cheating behaviors such as photos, videos and 3D molds are effectively resisted;
(3) and the space and time complexity of the system is optimized by adopting a system optimization algorithm, the response time of the system is reduced, and the fluency of the system is improved.
Drawings
Fig. 1 is a schematic structural diagram of a system according to an embodiment of the present invention.
FIG. 2 is a block flow diagram of a matching module in an embodiment of the invention.
The labels in the figure are: 1. an acquisition module; 2. an acquisition module; 3. an extraction module; 4. initializing a module; 5. a calculation module; 6. a matching module; 7. a detection module; 8. and an optimization module.
Detailed Description
Example (b): as shown in fig. 1, an embodiment of the present invention provides an online passing system based on a face-face feature recognition technology, which includes 8 modules, each of which is: the device comprises an acquisition module 1, an acquisition module 2, an extraction module 3, an initialization module 4, a calculation module 5, a matching module 6, a detection module 7 and an optimization module 8. The acquisition module 1 is connected with the acquisition module 2, the acquisition module 2 is connected with the extraction module 3, the extraction module 3 is connected with the initialization module 4, the initialization module 4 is connected with the calculation module 5, the calculation module 5 is connected with the matching module 6, the matching module 6 is connected with the detection module 7, and the detection module 7 is connected with the optimization module 8.
The acquisition module 1 is configured to acquire a face image to be recognized, where the face image acquired in this embodiment is a standard or non-standard real-time face image.
The acquisition module 2 is used for acquiring feature points in the acquired face image to be recognized, the selected feature points comprise key points and dense points, the key points comprise 5 points which are respectively a left eye center point, a right eye center point, a nose tip, a mouth left boundary point and a mouth right boundary point; the dense points are N points except the key point, the coordinates (x, y) of each dense feature point are calculated and then collected, and the calculation formula of the coordinates of the dense feature points is as follows:
x ═ 2 j-1. d/2+1 equation (1)
y ═ 2i-1 · d/2+1 equation (2)
In the formula, x is the abscissa of the dense feature point, y is the ordinate of the dense feature point, i is the ith row of dense feature point, j is the jth column of dense feature point, and d is the distance between two adjacent dense feature points.
The extraction module 3 sequentially extracts feature vectors of M ═ N +5 feature points according to the pixel value arrangement of each local region of the face image.
The initialization module 4 is configured to initialize weights and projection matrices of the M feature points.
The calculation formula of the weight is as follows:
in the formula WiIs the weight of the ith feature point, W0The initial weight of the ith feature point is 1, S by defaultiIs the shape vector of the ith feature point, piThe shape parameter corresponding to the ith feature point shape vector.
The calculation formula of the projection matrix is as follows:
in the formula PiFor projection matrices of feature points, arg is a function of the minimum of the projection, WiIs the weight of the i-th feature point, yiIs the ordinate, X, of the i-th feature pointiIs the ordinate of the ith feature point.
The calculating module 5 is configured to calculate weighted collaborative representation of the feature vectors of the M feature points to obtain a representation coefficient of the feature vector of the feature point, where a specific calculation formula is as follows:
wherein S is a coefficient, WiIs the weight of the ith feature point, PiIs a projection matrix of the feature points.
The matching module 6 is configured to match the acquired face image with an existing face database, and determine whether the matching is successful, and a main implementation flow thereof is as shown in fig. 2, and includes the following steps:
and the step S1 is used for reading the weights of M feature points in the face image collected on site, the projection matrix and the representation coefficients of the feature vectors thereof.
Step S2 is to traverse the existing face database, and sequentially calculate the weight of each face image feature point in the face database, the projection matrix, and the representation coefficient of its feature vector.
In step S3, a eigenface matching algorithm is used to compare the weights of M eigen points in the acquired face image, the projection matrix and the representation coefficients of their eigen vectors with the corresponding parameter values of each face image in the face database.
The step S4 is used to calculate the average similarity of the parameter values of the above two facial images.
Step S5 is used to determine whether the average similarity between the above parameter values is greater than or equal to 90%.
The step S6 is configured to determine whether the average similarity is greater than or equal to 90%, where if the average similarity is greater than or equal to 90%, the matching is successful, and otherwise the matching is failed.
Said step S7 is used to end the matching module 6 and enter the detection module 7.
The detection module 7 adopts online/offline living body detection service to identify whether the scanning behavior is personally operated.
The online/offline living body detection service prompts personnel who scan the human face on site to make corresponding actions according to the prompts, and judges whether the user is a living body or a real person by utilizing the background SDK to collect dynamic information in real time.
The optimization module 8 adopts a system optimization algorithm to optimize the system, so that the system response time is shortened, and the system fluency is improved.
The system optimization algorithm is a hierarchical retrieval identification method, which is characterized in that a relevant numerical parameter of a certain characteristic of a human face is used as a key word for preliminary retrieval, then images are numbered and sequenced in a corresponding subset, and then deep comparison is carried out to realize identity information authentication.
In summary, the invention relates to an online traffic system based on a face feature recognition technology, which adopts a feature face matching algorithm to realize face recognition with higher accuracy; the online/offline living body detection service is adopted to identify whether the user is a real person or not, and cheating tools such as photos, videos and 3D molds are effectively resisted; and the space and time complexity of the system is optimized by adopting a system optimization algorithm, the response time of the system is reduced, and the fluency of the system is improved. The face and facial feature recognition system can effectively improve the face recognition accuracy and the passing efficiency of the existing face recognition system, can be widely applied to the fields of financial industry, retail industry, security passing and the like, and meets the business requirements of identity verification, face attendance, gate passing and the like.
The foregoing is only a preferred embodiment of the present invention. The above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same, and the scope of the present invention is not limited to any such changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention.
Claims (1)
1. An online passing system based on a face and facial feature recognition technology is characterized by comprising: the device comprises an acquisition module, an extraction module, an initialization module, a calculation module, a matching module, a detection module and an optimization module; the acquisition module is connected with the acquisition module, the acquisition module is connected with the extraction module, the extraction module is connected with the initialization module, the initialization module is connected with the calculation module, the calculation module is connected with the matching module, the matching module is connected with the detection module, and the detection module is connected with the optimization module;
the acquisition module is used for acquiring a face image to be recognized, and the acquired face image is a standard or non-standard real-time face image;
the acquisition module is used for acquiring feature points in the acquired face image to be recognized, the selected feature points comprise key points and dense points, the key points comprise 5 points which are respectively a left eye center point, a right eye center point, a nose tip, a mouth left boundary point and a mouth right boundary point; the dense points are N points except the key point, the coordinates (x, y) of each dense feature point are calculated and then collected, and the calculation formula of the coordinates of the dense feature points is as follows:
x ═ 2 j-1. d/2+1 equation (1)
y ═ 2i-1 · d/2+1 equation (2)
In the formula, x is the abscissa of the dense feature points, y is the ordinate of the dense feature points, i is the ith row of dense feature points, j is the jth column of dense feature points, and d is the distance between two adjacent dense feature points;
the extraction module is used for sequentially extracting the feature vectors of the N +5 feature points according to the pixel value arrangement of each local area of the face image;
the initialization module is used for initializing and constructing the weights and the projection matrix of the M characteristic points;
the calculation formula of the weight is as follows:
in the formula WiIs the weight of the ith feature point, W0The initial weight of the ith feature point is 1, S by defaultiIs the shape vector of the ith feature point, piThe shape parameter corresponding to the ith characteristic point shape vector;
the calculation formula of the projection matrix is as follows:
in the formula PiFor projection matrices of feature points, arg is a function of the minimum of the projection, WiIs the weight of the i-th feature point, yiIs the ordinate, x, of the i-th feature pointiThe abscissa of the ith characteristic point is;
the calculation module is configured to calculate weighted collaborative representation of the feature vectors of the M feature points to obtain a representation coefficient of the feature vector of the feature point, where a specific calculation formula is:
wherein S is a coefficient, WiIs the weight of the ith feature point, PiA projection matrix of the characteristic points;
the matching module is used for matching the acquired face image with the existing face database and judging whether the matching is successful or not, and the implementation mainly comprises the following steps:
s1: reading weights of M characteristic points in a face image acquired on site, a projection matrix and representation coefficients of characteristic vectors of the projection matrix;
s2: traversing the existing face database, and sequentially calculating the weight of each face image feature point in the face database, a projection matrix and the expression coefficient of the feature vector of the projection matrix;
s3: comparing the weights of M characteristic points in the collected face images, the projection matrix and the expression coefficients of the characteristic vectors thereof with the corresponding parameter values of each face image in the face database by adopting a characteristic face matching algorithm;
s4: calculating the average similarity of various parameter values of the two human face images;
s5: judging whether the average similarity of the parameter values is more than or equal to 90%;
s6: if the average similarity is larger than or equal to 90%, the matching is successful, otherwise, the matching is failed;
s7: the matching module is finished;
the detection module adopts online/offline living body detection service to identify whether the scanning behavior is personal operation;
the online/offline living body detection service prompts personnel who scan the human face on site to make corresponding actions according to the prompt, and judges whether the user is a living body or a real person by utilizing the background SDK to collect dynamic information in real time;
the optimization module adopts a system optimization algorithm to optimize the system, so that the system response time is reduced, and the system fluency is improved;
the system optimization algorithm is a hierarchical retrieval identification method, which is characterized in that a relevant numerical parameter of a certain characteristic of a human face is used as a key word for preliminary retrieval, then images are numbered and sequenced in a corresponding subset, and then deep comparison is carried out to realize identity information authentication.
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