CN108549883A - A kind of face recognition methods again - Google Patents

A kind of face recognition methods again Download PDF

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Publication number
CN108549883A
CN108549883A CN201810486584.6A CN201810486584A CN108549883A CN 108549883 A CN108549883 A CN 108549883A CN 201810486584 A CN201810486584 A CN 201810486584A CN 108549883 A CN108549883 A CN 108549883A
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China
Prior art keywords
characteristic vector
depth characteristic
cosine similarity
distance metric
similarity measurement
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Chinese (zh)
Inventor
姚杨
姚一杨
张文杰
戴波
王彦波
梅峰
卢新岱
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State Grid Zhejiang Electric Power Co Ltd
Information and Telecommunication Branch of State Grid Zhejiang Electric Power Co Ltd
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State Grid Zhejiang Electric Power Co Ltd
Information and Telecommunication Branch of State Grid Zhejiang Electric Power Co Ltd
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Priority to CN201810486584.6A priority Critical patent/CN108549883A/en
Publication of CN108549883A publication Critical patent/CN108549883A/en
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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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/30—Scenes; Scene-specific elements in albums, collections or shared content, e.g. social network photos or video

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  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • General Health & Medical Sciences (AREA)
  • Human Computer Interaction (AREA)
  • Image Analysis (AREA)

Abstract

The present invention relates to field of face identification more particularly to a kind of recognition methods again of face, include the following steps：Training set A, B are obtained, using training set A, B training convolutional neural networks, depth characteristic vector set M, N is extracted respectively, establishes corresponding depth characteristic vector space S, D of depth characteristic vector set M, N；Learn distance metric and cosine similarity measurement；Calculate the distance metric for needing two facial images to be tested and cosine similarity measurement；Judge the similarity between two facial images in conjunction with distance metric and cosine similarity measurement.By using the present invention, following effect may be implemented：Distance metric is combined into the similarity between can more fully judging two width different images with cosine similarity measurement, it is more accurate to judge.

Description

A kind of face recognition methods again
Technical field
The present invention relates to field of face identification more particularly to a kind of recognition methods again of face.
Background technology
As people are paid more and more attention the concern of social public security and the development of mass data storage technology, peace Personnel in full monitoring system, which re-recognize, has become a hot issue.Angle and the light variation at different cameral visual angle are very Greatly, the appearance of personage may be changed so that it is still a challenging problem that personage, which re-recognizes,.
Precision of the identification technology in identification process be not high again by existing personnel.Such as application number： CN201710839181.0, denomination of invention：A kind of patent based on the face authentication method for quickly handling more learning distance metrics Apply, the technical solution recorded in the patent is：Enter training set, wherein training set is made of two subsets, the sample in subset S This is to (xi, xj) the same face is come from, the sample in subset D is to (xi, xj) from two different faces；To training subset Each of face image extract K kind features, indicate sample xiK-th of feature, k=1,2 ... ..., K；Learn K kind features Corresponding distance matrix metric and its weight；Two facial images are inputted as test sample；To two facial images of input Extract same K kinds feature, and using the distance matrix metric that has learnt and weight calculation this two facial images away from From d；Judge, is same person, if more than the second given threshold if the distance of two images is less than given first threshold Value, then be not same person.In the technical scheme, only similar and different face is trained, so identifying not Same angle shot, the different same person of face's light different photos when, be unable to get one and accurately judge structure.It is another Aspect by a single distance metric for find two width different images between difference and contact often it is unilateral.
Invention content
To solve the above problems, the present invention proposes a kind of face recognition methods again, the phase for judging two facial images Like degree.
A kind of face recognition methods again, includes the following steps：Obtain training set A, B, wherein the sample in training set A is The facial image that same person is shot by different angle under different light environments, the sample in training set B are logical for different people Cross the facial image that different angle is shot under different light environments；Using training set A, B training convolutional neural networks, carry respectively Depth characteristic vector set M, N are taken, corresponding depth characteristic vector space S, D of depth characteristic vector set M, N is established；Pass through depth spy Sign vector set M, N learn to obtain subspace W, pass through depth characteristic vector set M, N, subspace W, depth characteristic vector space S, D Calculate distance metric and cosine similarity measurement；Calculating needs the distance metric and cosine similarity of two facial images to be tested Measurement；Judge the similarity between two facial images in conjunction with the distance metric and cosine similarity measurement.
Preferably, the convolutional neural networks include pond layer, the first full articulamentum and the second full articulamentum, and described first Full articulamentum includes 1536 neurons, and the neuronal quantity of the second full articulamentum is equal to the number of pedestrian image in training set Amount.
Preferably, the calculating distance metric includes：The calculation formula of distance metric is：
Wherein,xi,yjIt is the depth characteristic vector of image i and j, I ∈ A, j ∈ B, xi∈ N, yj∈M。
Preferably, the calculating cosine similarity measurement includes：With a depth characteristic in depth characteristic vector space S The cosine value of the angle of a depth characteristic vector y in vector x and depth characteristic vector space D is measured as cosine similarity dcos(x,y)。
Preferably, distance metric described in the combination and cosine similarity are measured to judge the phase between two facial images Include like degree：The distance matrix d (x, y) after distance metric and cosine similarity measurement fusion is calculated,
D (x, y)=dW(x,y)+dcos(x,y)
Given threshold T, if the result of calculation of distance matrix d (x, y) is more than threshold value T, two facial image dissmilarities are Different people；Otherwise, it means that two facial images are similar, it is same person.
By using the present invention, following effect may be implemented：
1. the facial image of different angle shooting and different light shootings is trained by convolutional neural networks, When identifying the facial image that different angle shooting and different light are shot, the accuracy of discrimination is improved；
2. distance metric and cosine similarity measurement are combined between can more fully judging two width different images Similarity, judge it is more accurate.
Description of the drawings
The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
Fig. 1 is the flow diagram of the present invention.
Specific implementation mode
Below in conjunction with attached drawing, technical scheme of the present invention will be further described, but the present invention is not limited to these realities Apply example.
The basic thought of the present invention is trained to the facial image shot in different angle shooting and different light, To improve in the human face photo under identifying varying environment, robustness is good；In addition, not being to judge two width not according to distance metric With the similarity between image, but between being combined by distance metric and cosine similarity measurement and judging different images Similarity.
A kind of face recognition methods again, includes the following steps：
Step 1 obtains training set A, B, wherein the sample in training set A be same person by different angle in difference The facial image shot under light environment, the sample in training set B be different people by different angle under different light environments The facial image of shooting.In the discrimination of actual face, handled facial image is often different angle shooting, due to being Different angle shooting, cause the light of face face different, i.e. the difference of lightness and darkness.Consider these discrimination factors, it will Training set A, B are trained by convolutional neural networks, are more corresponded to actual needs, while improving the accuracy of discrimination.
Step 2: using training set A, B training convolutional neural networks, depth characteristic vector set M, N are extracted respectively, are established deep Spend corresponding depth characteristic vector space S, D of set of eigenvectors M, N.Trained convolutional neural networks include pond layer, first complete Articulamentum and the second full articulamentum, the first full articulamentum include 1536 neurons, the nerve of the second full articulamentum First quantity is equal to the quantity of pedestrian image in training set.Wherein, the number parameter of the first full articulamentum oneself setting as needed； Second full articulamentum plays the role of classification in entire convolutional neural networks；The quantity of second full articulamentum is training set A, B In image number；Then fc7 layers in the second full articulamentum of output is extracted as depth characteristic vector.
Step 3 learns to obtain subspace W by depth characteristic vector set M, N, passes through depth characteristic vector set M, N, son Space W, depth characteristic vector space S, D calculate distance metric and cosine similarity measurement.Specifically, the calculating of distance metric is public Formula is：
Wherein,xi,yjIt is the depth characteristic vector of image i and j, I ∈ A, j ∈ B, xi∈ N, yj∈M.The calculation formula of subspace W is：W=(w1,w2,…,wr)∈Rd×r, d expression primitive characters The dimension in space, r indicate the dimension in transfer characteristic space.
Specifically, the computational methods of cosine similarity measurement are：With a depth characteristic in depth characteristic vector space S The cosine value of the angle of a depth characteristic vector y in vector x and depth characteristic vector space D is measured as cosine similarity dcos(x, y),
Step 4 calculates the distance metric for needing two facial images to be tested and cosine similarity measurement.In step 1 To in four, describes to obtain distance metric and the process of cosine similarity measurement obtains need according to the above process in this step The distance metric and cosine similarity of two facial images to be tested are measured.
Step 5, it is similar between two facial images to judge in conjunction with the distance metric and cosine similarity measurement Degree.Specifically：The distance matrix d (x, y) after distance metric and cosine similarity measurement fusion is calculated,
D (x, y)=dW(x,y)+dcos(x,y)
Given threshold T, if the result of calculation of distance matrix d (x, y) is more than threshold value T, two facial image dissmilarities are Different people；Otherwise, it means that two facial images are similar, it is same person.
Cosine similarity measurement is to use two depth characteristic vectorial angle cosine values as poor between two images of measurement Different size more focuses on difference of two vectors on direction, while correcting that module that may be present is skimble-scamble to ask Topic.Distance metric and cosine similarity measurement are combined together, considered on common distance metric angle because Element, therefore result is more accurate.
By using the present invention, following effect may be implemented：
1. the facial image of different angle shooting and different light shootings is trained by convolutional neural networks, When identifying the facial image that different angle shooting and different light are shot, the accuracy of discrimination is improved；
2. distance metric and cosine similarity measurement are combined between can more fully judging two width different images Similarity, judge it is more accurate.
Those skilled in the art can make various modifications to described specific embodiment Or supplement or substitute by a similar method, however, it does not deviate from the spirit of the invention or surmounts the appended claims determines The range of justice.

Claims (4)

1. a kind of recognition methods again of face, which is characterized in that include the following steps：
Obtain training set A, B, wherein the sample in training set A be same person by different angle under different light environments The facial image of shooting, the sample in training set B are the face that different people is shot by different angle under different light environments Image；
Using training set A, B training convolutional neural networks, depth characteristic vector set M, N are extracted respectively, establish depth characteristic vector Collect corresponding depth characteristic vector space S, D of M, N；
Learn to obtain subspace W by depth characteristic vector set M, N, it is special by depth characteristic vector set M, N, subspace W, depth It levies vector space S, D and calculates distance metric and cosine similarity measurement；
Calculate the distance metric for needing two facial images to be tested and cosine similarity measurement；
Judge the similarity between two facial images in conjunction with the distance metric and cosine similarity measurement.
2. face according to claim 1 recognition methods again, which is characterized in that the calculating distance metric includes：Distance The calculation formula of measurement is：
Wherein,xi,yjIt is the depth characteristic vector of image i and j, i ∈ A, j ∈ B, xi∈ N, yj∈M。
3. face according to claim 1 recognition methods again, which is characterized in that the calculating cosine similarity measurement packet It includes：With a depth spy in the depth characteristic vector x and depth characteristic vector space D in depth characteristic vector space S The cosine value for levying the angle of vector y measures d as cosine similaritycos(x,y)。
4. face according to claim 2 or 3 recognition methods again, which is characterized in that distance metric described in the combination and Cosine similarity measurement judges that the similarity between two facial images includes：
The distance matrix d (x, y) after distance metric and cosine similarity measurement fusion is calculated,
D (x, y)=dW(x,y)+dcos(x,y)
Given threshold T, if the result of calculation of distance matrix d (x, y) is more than threshold value T, two facial image dissmilarities, for difference People；Otherwise, it means that two facial images are similar, it is same person.
CN201810486584.6A 2018-08-06 2018-08-06 A kind of face recognition methods again Pending CN108549883A (en)

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CN109726756A (en) * 2018-12-25 2019-05-07 北京旷视科技有限公司 Image processing method, device, electronic equipment and storage medium
CN111626567A (en) * 2020-04-30 2020-09-04 中国直升机设计研究所 Identification and calculation method for guaranteeing resource similarity
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CN109543611A (en) * 2018-11-22 2019-03-29 珠海市蓝云科技有限公司 A method of the images match based on artificial intelligence
CN109726756A (en) * 2018-12-25 2019-05-07 北京旷视科技有限公司 Image processing method, device, electronic equipment and storage medium
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CN113361301A (en) * 2020-03-04 2021-09-07 上海分众软件技术有限公司 Advertisement video identification method based on deep learning
CN113362096A (en) * 2020-03-04 2021-09-07 驰众信息技术（上海）有限公司 Frame advertisement image matching method based on deep learning
CN111626567A (en) * 2020-04-30 2020-09-04 中国直升机设计研究所 Identification and calculation method for guaranteeing resource similarity

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