CN116863547B - Multi-mode biological identification method and system based on feature scoring - Google Patents
Multi-mode biological identification method and system based on feature scoring Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 23
- 210000001508 eye Anatomy 0.000 claims description 33
- 210000004709 eyebrow Anatomy 0.000 claims description 33
- 210000001331 nose Anatomy 0.000 claims description 18
- 230000001815 facial effect Effects 0.000 claims description 6
- 210000000214 mouth Anatomy 0.000 claims description 6
- 208000012880 Finger injury Diseases 0.000 description 1
- 208000011092 Hand injury Diseases 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
- G06V40/70—Multimodal biometrics, e.g. combining information from different biometric modalities
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
- G06V10/462—Salient features, e.g. scale invariant feature transforms [SIFT]
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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
- G06V40/1347—Preprocessing; Feature extraction
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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
- G06V40/18—Eye characteristics, e.g. of the iris
- G06V40/193—Preprocessing; Feature extraction
Abstract
The invention relates to the technical field of biological feature recognition, in particular to a multi-mode biological recognition method and a multi-mode biological recognition system based on feature scores, wherein the method comprises the steps of firstly collecting biological features of a person to be recognized, including a face photo, a fingerprint photo and a palmprint photo of the person to be recognized; scoring the characteristics required by each biological identification mode according to the acquired photos; respectively carrying out biological feature recognition by selecting two modes with highest scores; if the two identification modes with the highest scores pass through identification, the identity authentication is considered to pass; otherwise, the identity authentication is not considered to pass. The invention scores each biological recognition mode according to the image, and decides what mode is selected for the identity authentication according to the score, so as to solve the problem that the identity authentication fails because some recognition modes can not be completed under some conditions.
Description
Technical Field
The invention relates to the technical field of biological feature recognition, in particular to a multi-mode biological recognition method and system based on feature scoring.
Background
Along with the development of the biometric technology, the way of identity authentication through single-mode biometric identification cannot meet the needs of some scenes in terms of safety and reliability, so that the application of multi-mode biometric identification is more and more widespread, and the current multi-mode biometric identification is usually formed by combining two or more single-mode identifications, such as face identification and fingerprint identification combination, iris identification and vein identification combination and the like, and the passing of the identity authentication is judged under the condition that two or more identification ways can pass.
However, when the person to be identified fails to perform a certain identification mode due to a certain situation, the identity authentication will fail. Such as when the person to be identified needs to wear a sunglasses and cannot perform iris recognition, or when the person to be identified cannot perform fingerprint recognition due to hand injury.
For example, in the chinese patent application publication No. CN 107911371A and application publication No. 2018.04.13, an identity authentication method and system based on multiple biological characteristics are disclosed, where a unique biological characteristic information base is established by collecting palm print/fingerprint and iris information of a user and storing the palm print/fingerprint and iris information in an authentication server, then palm print/fingerprint authentication is performed, palm print/fingerprint information of the user is collected to perform palm print/fingerprint information matching, if matching is successful, iris information of the user is collected to perform iris information collection authentication, and iris information is sent to the authentication server to perform matching, if matching is successful, final authentication is successful; if the authentication fails, the authentication fails. The invention fuses the palm print/fingerprint recognition technology and the iris recognition biological recognition technology together to carry out identity authentication, thereby greatly improving the reliability and the safety of an identity authentication system.
Disclosure of Invention
In order to avoid the problems of the prior art, the invention aims to provide a multi-mode biological identification method and a system based on feature scoring.
In order to achieve the above purpose, the present invention provides the following technical solutions: a multi-modal biometric method based on feature scoring, comprising the steps of:
s1: firstly, pre-collecting biological characteristics of a person to be identified, including a face photo, a fingerprint photo and a palmprint photo of the person to be identified;
s2: scoring the characteristics required by each biological recognition mode according to the photos acquired in the step S1; scoring the required characteristics of the face recognition and iris recognition modes based on a Dlib algorithm; scoring the characteristics required by the fingerprint identification and palmprint identification modes based on a sift algorithm;
s3: according to the scores of the characteristics required by the biological recognition modes in the step S2, selecting two modes with the highest scores to respectively perform biological feature recognition;
s4: if the two identification modes with the highest scores pass through identification, the identity authentication is considered to pass; otherwise, the identity authentication is not considered to pass.
The invention is further configured to score the face recognition score in step S2, and specifically includes the following steps:
s211: obtaining a face key point coordinate set through a model of a 68 key point of a Dlib algorithm;
s212: classifying different positions of eyes, eyebrows, noses and mouths in the key point coordinate set according to the relative positions and the shapes of the key points of the eyes, the eyebrows, the noses and the mouths to obtain a key point set of the eyes, a key point set of the eyebrows, a key point set of the noses and a key point set of the mouths;
s213: according to the actual acquired key point number Nb of eyebrows, the actual acquired key point number Ne of eyes, the actual acquired key point number NN of noses and the actual acquired key point number Nm of mouths, the score X of face recognition is obtained according to the following formula:
wherein A, B, C, D is the weight of the key points of the eyebrow, eye, nose and mouth parts in the face recognition score, and a+b+c+d=100.
Under the condition that the face is complete and free of shielding, 68 key points can be obtained through a model of 68 key points of a Dlib algorithm, wherein 10 key points of eyebrows, 12 key points of eyes, 9 key points of a nose and 20 key points of a mouth are obtained. Therefore, whether the face is complete or not can be preliminarily judged according to the obtained number of key points of each part, and whether the key parts are shielded or not can be preliminarily judged.
If the face is not shielded and damaged, 68 key points are all identified and substituted into the formula, and the face recognition score X is 100 points. The higher the degree to which the face is blocked, the fewer key points that can be identified, the lower the face recognition score X.
The invention further provides that the step S2 scores the iris recognition score, which specifically comprises the following steps:
s221: obtaining a face key point coordinate set through a model of a 68 key point of a Dlib algorithm; under the condition that the face is complete and free of shielding, 10 key points with eyebrows and 12 key points with eyes are arranged in 68 key points;
s222: classifying different parts of eyes and eyebrows in the key point set according to the relative positions and the shape of the key points of the eyes and the eyebrows, obtaining iris recognition score Y through the following formula,
the number of actually obtained keypoints of the eyebrows is Nb, the number of actually obtained keypoints of the eyes is Ne, E and F are weights of the keypoints of the eyebrows and the eyes in the iris recognition score, respectively, and e+f=100.
The invention further provides that the step S2 scores the fingerprint identification score, which specifically comprises the following steps:
s231: and extracting a minutiae set of the fingerprint by a sift algorithm according to the obtained fingerprint photo.
S232: according to the obtained number of fingerprint minutiae points, the fingerprint recognition algorithm score Z is obtained by the following formula:
and Nf is the number of the actually acquired fingerprint minutiae, and Gf is the threshold value of the fingerprint identification minutiae.
In judicial authentication, two fingerprints are considered to be matching if their number of minutiae on matching is greater than a threshold. Thus, if the number of minutiae extracted exceeds the minutiae threshold Gf, the image may be considered to provide sufficient complete information for fingerprint retrieval and identification.
The invention further provides that the step S2 of scoring the palmprint recognition score specifically comprises the following steps:
s241: extracting a minutiae set of the palmprint by a sift algorithm according to the obtained palmprint photo;
s242: according to the obtained number of the palmprint minutiae, the palmprint recognition algorithm score W is obtained by the following formula:
wherein Np is the number of actually acquired palmprint minutiae, and Gp is the palmprint recognition minutiae threshold.
The invention also provides a multi-mode biological recognition system based on the feature score, which comprises:
the biological information base is used for storing facial feature information, palmprint/fingerprint feature information and iris information of the user;
the biological information acquisition module is used for acquiring the biological characteristic information required by the multi-mode biological identification method based on the characteristic score; the device comprises a facial information acquisition module, a palm print/fingerprint acquisition module and an iris information acquisition module;
the biological characteristic information matching module is used for comparing the collected biological characteristic information of the user with the information in the biological information base in an authentication way, and matching the information;
and the authentication module is used for carrying out identity authentication according to the information matching result.
The present invention is further configured such that the multi-modal biometric system further includes at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the processor, the instructions being executable by the at least one processor, to cause the at least one processor to perform a multi-modal biometric method based on feature scores as described above.
In summary, the technical scheme of the invention has the following beneficial effects:
1. according to the invention, biological characteristics such as images of the face and the hand are pre-collected, each biological identification mode is scored according to the images, and then the mode of the identity authentication is determined according to the score, so that the problem that in certain cases, certain identification modes cannot be completed, and the identity authentication fails is solved. For example, if finger injury cannot complete fingerprint recognition or palm print recognition, another two mode authentication modes are selected for authentication according to the scores.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a multi-modal scoring-based biometric identification method according to the present invention.
Detailed Description
In order to make the technical solution of the present invention better understood by those skilled in the art, the technical solution of the present invention will be clearly and completely described in the following with reference to the accompanying drawings, and based on the embodiments of the present invention, other similar embodiments obtained by those skilled in the art without making any inventive effort should be included in the scope of protection of the present invention.
The invention will be further described with reference to the drawings and preferred embodiments.
Example 1:
referring to fig. 1, in a preferred embodiment of the present invention, a multi-modal biometric method based on feature scores includes the following steps:
s1: firstly, pre-collecting biological characteristics of a person to be identified, including a face photo, a fingerprint photo and a palmprint photo of the person to be identified;
s2: scoring the characteristics required by each biological recognition mode according to the photos acquired in the step S1; scoring the required characteristics of the face recognition and iris recognition modes based on a Dlib algorithm; scoring the characteristics required by the fingerprint identification and palmprint identification modes based on a sift algorithm;
the score scoring of the required characteristics of the face recognition mode specifically comprises the following steps:
s211: obtaining a face key point coordinate set through a model of a 68 key point of a Dlib algorithm;
s212: classifying different positions of eyes, eyebrows, noses and mouths in the key point coordinate set according to the relative positions and the shapes of the key points of the eyes, the eyebrows, the noses and the mouths to obtain a key point set of the eyes, a key point set of the eyebrows, a key point set of the noses and a key point set of the mouths;
s213: according to the actual acquired key point number Nb of eyebrows, the actual acquired key point number Ne of eyes, the actual acquired key point number NN of noses and the actual acquired key point number Nm of mouths, the score X of face recognition is obtained according to the following formula:
wherein A, B, C, D is the weight of the key points of the eyebrow, eye, nose and mouth parts in the face recognition score, and a+b+c+d=100.
Under the condition that the face is complete and free of shielding, 68 key points can be obtained through a model of 68 key points of a Dlib algorithm, wherein 10 key points of eyebrows, 12 key points of eyes, 9 key points of a nose and 20 key points of a mouth are obtained. Therefore, whether the face is complete or not can be preliminarily judged according to the obtained number of key points of each part, and whether the key parts are shielded or not can be preliminarily judged.
If the face is not shielded and damaged, 68 key points are all identified and substituted into the formula, and the face recognition score X is 100 points. The higher the degree to which the face is blocked, the fewer key points that can be identified, the lower the face recognition score X.
The score scoring of the required characteristics of the iris recognition mode specifically comprises the following steps:
s221: obtaining a face key point coordinate set through a model of a 68 key point of a Dlib algorithm; under the condition that the face is complete and free of shielding, 10 key points with eyebrows and 12 key points with eyes are arranged in 68 key points;
s222: classifying different parts of eyes and eyebrows in the key point set according to the relative positions and the shape of the key points of the eyes and the eyebrows, obtaining iris recognition score Y through the following formula,
the number of actually obtained keypoints of the eyebrows is Nb, the number of actually obtained keypoints of the eyes is Ne, E and F are weights of the keypoints of the eyebrows and the eyes in the iris recognition score, respectively, and e+f=100.
The scoring of the feature score required by the fingerprint identification mode specifically comprises the following steps:
s231: and extracting a minutiae set of the fingerprint by a sift algorithm according to the obtained fingerprint photo.
S232: according to the obtained number of fingerprint minutiae points, the fingerprint recognition algorithm score Z is obtained by the following formula:
and Nf is the number of the actually acquired fingerprint minutiae, and Gf is the threshold value of the fingerprint identification minutiae.
In judicial authentication, two fingerprints are considered to be matching if their number of minutiae on matching is greater than a threshold. Thus, if the number of minutiae extracted exceeds the minutiae threshold Gf, the image may be considered to provide sufficient complete information for fingerprint retrieval and identification.
Similarly, the palm print identification required feature score grading specifically comprises the following steps:
s241: extracting a minutiae set of the palmprint by a sift algorithm according to the obtained palmprint photo;
s242: according to the obtained number of the palmprint minutiae, the palmprint recognition algorithm score W is obtained by the following formula:
wherein Np is the number of actually acquired palmprint minutiae, and Gp is the palmprint recognition minutiae threshold.
S3: according to the scores of the characteristics required by the biological recognition modes in the step S2, selecting two modes with the highest scores to respectively perform biological feature recognition;
s4: if the two identification modes with the highest scores pass through identification, the identity authentication is considered to pass; otherwise, the identity authentication is not considered to pass.
Example 2:
the invention also provides a multi-mode biological recognition system based on the feature scores, and the multi-mode biological recognition method based on the feature scores described in the embodiment 1 comprises the following steps:
the biological information base is used for storing facial feature information, palmprint/fingerprint feature information and iris information of the user;
the biological information acquisition module is used for acquiring the biological characteristic information required by the multi-mode biological identification method based on the characteristic score; the device comprises a facial information acquisition module, a palm print/fingerprint acquisition module and an iris information acquisition module;
the biological characteristic information matching module is used for comparing the collected biological characteristic information of the user with the information in the biological information base in an authentication way, and matching the information;
and the authentication module is used for carrying out identity authentication according to the information matching result.
The multi-modal biometric system further includes at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the processor, the instructions being executable by the at least one processor, to cause the at least one processor to perform a multi-modal biometric method based on feature scores as described above.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above examples, and all technical solutions belonging to the concept of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to the present invention may occur to one skilled in the art without departing from the principles of the present invention and are intended to be within the scope of the present invention.
Claims (7)
1. A multi-modal biometric method based on feature scoring, comprising the steps of:
s1: firstly, pre-collecting biological characteristics of a person to be identified, including a face photo, a fingerprint photo and a palmprint photo of the person to be identified;
s2: scoring the characteristics required by each biological recognition mode according to the photos acquired in the step S1; scoring the required characteristics of the face recognition and iris recognition modes based on a Dlib algorithm; scoring the characteristics required by the fingerprint identification and palmprint identification modes based on a sift algorithm;
the score X of face recognition is obtained by the following formula:
wherein A, B, C, D is the weight of the key points of the eyebrow, eye, nose and mouth parts in the face recognition score, and a+b+c+d=100; the number of actually acquired key points of the eyebrows is Nb, the number of actually acquired key points of eyes is Ne, the number of actually acquired key points of the nose is NN, and the number of actually acquired key points of the mouth is Nm;
the iris recognition score Y is obtained by the following formula,
the number of actually obtained key points of the eyebrows is Nb, the number of actually obtained key points of the eyes is Ne, E and F are weights of the key points of the eyebrows and the eyes in iris recognition scoring scores respectively, and E+F=100;
the fingerprint recognition algorithm score Z is obtained from the following formula:
wherein Nf is the number of actually acquired fingerprint minutiae, and Gf is a fingerprint identification minutiae threshold;
the palmprint recognition algorithm score W is obtained from the following formula:
wherein Np is the number of actually acquired palmprint minutiae, and Gp is a palmprint recognition minutiae threshold;
s3: according to the scores of the characteristics required by the biological recognition modes in the step S2, selecting two modes with the highest scores to respectively perform biological feature recognition;
s4: if the two identification modes with the highest scores pass through identification, the identity authentication is considered to pass; otherwise, the identity authentication is not considered to pass.
2. The multi-modal biometric identification method based on feature scoring according to claim 1, wherein the scoring of the face recognition score in step S2 specifically comprises the steps of:
s211: obtaining a face key point coordinate set through a model of a 68 key point of a Dlib algorithm;
s212: classifying different positions of eyes, eyebrows, noses and mouths in the key point coordinate set according to the relative positions and the shapes of the key points of the eyes, the eyebrows, the noses and the mouths to obtain a key point set of the eyes, a key point set of the eyebrows, a key point set of the noses and a key point set of the mouths;
s213: and calculating the face recognition score X according to the actual acquired key point number Nb of the eyebrows, the actual acquired key point number Ne of eyes, the actual acquired key point number NN of the nose and the actual acquired key point number Nm of the mouth.
3. The multi-modal biometric identification method based on feature scoring according to claim 1, wherein the scoring of the iris recognition score in step S2 comprises the steps of:
s221: obtaining a face key point coordinate set through a model of a 68 key point of a Dlib algorithm; under the condition that the face is complete and free of shielding, 10 key points with eyebrows and 12 key points with eyes are arranged in 68 key points;
s222: and classifying different parts of eyes and eyebrows in the key point set according to the relative positions and the shape of the key points of the eyes and the eyebrows, and calculating iris recognition scores Y.
4. The multi-modal biometric method based on feature scoring as recited in claim 1, wherein the scoring of the fingerprint score in step S2 comprises the steps of:
s231: extracting a minutiae set of the fingerprint by a sift algorithm according to the obtained fingerprint photo;
s232: and calculating the fingerprint identification algorithm score Z according to the number of the obtained fingerprint minutiae points.
5. The multi-modal biometric method based on feature scoring as recited in claim 1, wherein the step S2 of scoring the palmprint recognition score comprises the steps of:
s241: extracting a minutiae set of the palmprint by a sift algorithm according to the obtained palmprint photo;
s242: and calculating the palmprint recognition algorithm score W according to the obtained palmprint minutiae number.
6. A feature-scoring-based multi-modal biometric system employing a feature-scoring-based multi-modal biometric method as in any one of claims 1-5, comprising:
the biological information base is used for storing facial feature information, palmprint/fingerprint feature information and iris information of the user;
a biological information acquisition module for acquiring biological characteristic information required in the multi-mode biological identification method based on the characteristic score according to any one of claims 1 to 5; the device comprises a facial information acquisition module, a palm print/fingerprint acquisition module and an iris information acquisition module;
the biological characteristic information matching module is used for comparing the collected biological characteristic information of the user with the information in the biological information base in an authentication way, and matching the information;
and the authentication module is used for carrying out identity authentication according to the information matching result.
7. The feature-scoring-based multi-modal biometric system of claim 6, wherein the multi-modal biometric system further comprises at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the processor to cause the at least one processor to perform a feature scoring-based multi-modal biometric method of any one of claims 1-5.
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| CN1794266A (en) * | 2005-12-31 | 2006-06-28 | 清华大学 | Biocharacteristics fusioned identity distinguishing and identification method |
| CN107911371A (en) * | 2017-11-23 | 2018-04-13 | 郑州云海信息技术有限公司 | A kind of identity identifying method and system based on a variety of biological characteristics |
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| CN103886283A (en) * | 2014-03-03 | 2014-06-25 | 天津科技大学 | Method for fusing multi-biometric image information for mobile user and application thereof |
| CN110569756B (en) * | 2019-08-26 | 2022-03-22 | 长沙理工大学 | Face recognition model construction method, recognition method, device and storage medium |
| US20230206700A1 (en) * | 2021-12-29 | 2023-06-29 | Elm | Biometric facial recognition and liveness detector using ai computer vision |
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| CN1794266A (en) * | 2005-12-31 | 2006-06-28 | 清华大学 | Biocharacteristics fusioned identity distinguishing and identification method |
| CN107911371A (en) * | 2017-11-23 | 2018-04-13 | 郑州云海信息技术有限公司 | A kind of identity identifying method and system based on a variety of biological characteristics |
| CN110119610A (en) * | 2019-05-23 | 2019-08-13 | 湖北东方星海科技实业有限公司 | A kind of multiple-biological characteristic information safety certification detection method |
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