CN116343313A - Face recognition method based on eye features - Google Patents
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Abstract
The invention discloses a face recognition method based on eye features, which belongs to the technical field of image processing, and aims to comprehensively carry out face recognition by utilizing the difference of the color distribution of each eye contour and the imaging of eyes when the matching degree is higher than a matching threshold value, wherein the eye contour features and the color distribution features are respectively extracted from the shot face images, and the eye contour features and the color distribution features are respectively compared with stored face features to obtain the matching degree.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a face recognition method based on eye features.
Background
In the existing face recognition method, face images are collected, the face images are processed, face feature data are extracted, and a neural network is adopted to classify the face feature data, so that face recognition is realized. However, in many cases, the person only exposes the eyes and the face is blocked by the mask or the face mask, and therefore, a large amount of face information is hidden, and face recognition cannot be performed, so that authentication cannot be performed.
Disclosure of Invention
Aiming at the defects in the prior art, the face recognition method based on the eye features solves the problem that face recognition cannot be performed when a large amount of face information is hidden.
In order to achieve the aim of the invention, the invention adopts the following technical scheme: a face recognition method based on eye features comprises the following steps:
s1, intercepting an eye image on a face image;
s2, extracting eye contour features and color distribution features from the eye images respectively;
s3, matching the eye contour features and the color distribution features with the stored facial image features respectively to obtain matching degree;
and S4, when the matching degree is higher than the matching threshold, the face recognition is successful.
Further, the step S2 includes the following sub-steps:
s21, filtering the eye image to obtain a filtered image;
s22, partitioning the filtered image to obtain a plurality of eye subareas;
s23, extracting color distribution values from each eye subarea, and constructing a color distribution matrix of each eye subarea;
s24, obtaining color distribution characteristics according to the color distribution matrix of the eye subareas;
s25, carrying out graying treatment on the filtered eye image to obtain a gray image;
s26, extracting contour features from the gray level map to obtain eye contour features.
The beneficial effects of the above-mentioned further scheme are: the invention carries out filtering treatment on the eye image to filter the influence of abnormal pixel points, then carries out partition on the image, extracts color distribution values in each eye subarea, constructs a color distribution matrix for expressing the color distribution condition in one area, carries out graying treatment on the filtered eye image, extracts the outline, reduces the data quantity and simultaneously extracts effective data.
Further, the filtering formula in S21 is:
wherein ,for filtering the picture->Pixel value of each pixel, +.>For the filtering factor +.>For the first part on the eye image>Pixel value of each pixel, +.>For +.>A peripheral part centered on the pixel of (2)>Pixel values of the pixel points, ||is absolute value operation, and +.>As an arctangent function.
The further scheme is thatThe beneficial effects are as follows: the invention uses the characteristic of the arctangent function, takes each pixel point on the eye image as the center, discards the sliding filtering mode, and uses the characteristic of the arctangent function to obtain the sliding filtering modeWhen the difference between the pixel value and the surrounding pixel value is large, the pixel value of the surrounding pixel point is considered in the filtering process, so that the noise point can be filtered out, when the pixel value of the surrounding pixel point is large, the surrounding pixel point is also the surrounding pixel point, the characteristic that the pixel value is not influenced by the surrounding pixel point is considered, and when the difference between the pixel value and the surrounding pixel point is small, the pixel value is not influenced by the surrounding pixel point>The peripheral pixel values are not affected by the original characteristics of the pixel, and the filtering factor +.>For adjustable parameters, by adjusting the filter factor +.>The filtering effect can be adjusted.
Further, the color distribution matrix in S23 is:
wherein ,is a color distribution matrix>For a first class of color distribution values of the 1 st color component in the eye subregion,for the second type of color distribution value of the 1 st color component in the eye subregion +.>A third class of color distribution values for the 1 st color component in the eye subregion>A fourth class of color distribution values for the 1 st color component in the eye subregion>Is the eye subregion->Color distribution values of the first type of the individual color components, respectively>Is the eye subregion->Second class of color distribution values of the individual color components, respectively>Is the eye subregion->Third class of color distribution values of the individual color components, respectively>Is the eye subregion->Fourth class of color distribution values of the individual color components, respectively>Is the eye subregion->Color distribution values of the first type of the individual color components, respectively>Is the eye subregion->Second class of color distribution values of the individual color components, respectively>Is the eye subregion->Third class of color distribution values of the individual color components, respectively>Is the eye subregion->Fourth class of color distribution values of the individual color components, respectively>Is the number of color components;
the calculation formula of the color distribution value is:
wherein ,is the eye subregion->The +.>Color component->Is the pixel point in the eye subareaIs a number of (3).
The beneficial effects of the above further scheme are: the invention shows the color distribution characteristics through four types of color distribution values of a plurality of color components, thereby deeply representing the characteristics of eyes after each human eye is imaged.
Further, the step S26 includes the following substeps:
s261, calculating gray characteristic values of all pixel points on a gray map, and taking the pixel points as pixel points to be contoured when the gray characteristic values are larger than a gray characteristic threshold value;
s262, eliminating isolated pixel points to be contoured according to the position distribution of the pixel points to be contoured to obtain eye contour feature pixel points;
s263, partitioning all the eye contour feature pixel points according to the eye contour feature pixel point positions to obtain contour sub-areas;
s264, calculating the eye contour feature of each contour subarea.
The beneficial effects of the above further scheme are: the method comprises the steps of firstly calculating the gray characteristic value of each pixel point, when the gray characteristic value is larger than a gray threshold value, determining the pixel point as a suspected outline pixel point, deleting isolated points according to whether the suspected outline pixel points are continuous in position, filtering the suspected outline pixel points, and partitioning to obtain the eye outline characteristics of the subareas.
Further, the calculation formula of the gray characteristic value in S261 is:
wherein ,is the (th) on the gray level diagram>Gray characteristic value of each pixel point, +.>Is the (th) on the gray level diagram>Gray value of each pixel, +.>Is the (th) on the gray level diagram>The +.>Gray value of each pixel, +.>Is the maximum gray distance value,/>Is the minimum gray distance value,/>Is the (th) on the gray level diagram>The gray value of the 1 st pixel around the pixel,is the (th) on the gray level diagram>Gray value of 8 th pixel around each pixel, absolute value operation,/-for the 8 th pixel>For maximum value of the sequence, +.>For the minimum of the sequence.
The beneficial effects of the above further scheme are: the invention takes each pixel point on the gray level graph as the center, calculates the gray level characteristic value of each pixel point, the gray level characteristic value is used for evaluating the difference between the gray level characteristic value and the peripheral pixel points, when the gray level difference total value between the center pixel point and the peripheral pixel points is larger, the position where the gray level value is suspected to be obviously changed is indicated, therefore, the point can be a pixel point on the outline, the difference between the maximum gray level distance value and the minimum gray level distance value is found by judging the gray level distance value of the pixel point and the peripheral pixel point, if the difference between the maximum gray level distance value and the minimum gray level distance value is larger, the pixel point on the outline is further embodied, and the pixel point with the gray level characteristic value larger than the gray level characteristic threshold value is directly selected according to the gray level characteristic values of all the pixel points, so as to obtain the outline pixel point.
Further, the formula for calculating the eye contour feature in S264 is:
wherein ,for eye contour features,/->Is the>Distance from each eye contour feature pixel point to the fixed eye contour feature pixel point, +.>For the number of eye contour feature pixels in the contour subregion, +.>For distance->Maximum distance in->For distance->Is the minimum distance of (a).
The beneficial effects of the above further scheme are: the fixed eye contour feature pixel point is a pixel point in the contour sub-region, which is used as a reference pixel point, the distances from other pixel points to the reference pixel point are calculated, and according to each pixel pointAnd->The difference between them shows the distance distribution and at the same time according to the maximum distance +.>And->Difference, sum->And->The difference reflects the situation among the maximum distance, the average distance and the minimum distance, and further highlights the position distribution situation of the pixel points on each contour sub-region.
Further, the step S3 includes the following sub-steps:
s31, expanding each color distribution matrix in the color distribution characteristics to form a color distribution vector;
s32, calculating the similarity between the color distribution vector and the color distribution vector in the stored facial image characteristics to obtain a first similarity;
s33, calculating the similarity of the eye contour features and the eye contour features in the stored facial image features to obtain a second similarity;
s34, calculating the matching degree according to the first similarity and the second similarity.
Further, the formula for obtaining the first similarity in S32 is as follows:
wherein ,is->First similarity, < >>Is->Color distribution vector->For store->Color distribution vector->For dot multiplication, ->Is cross-multiplied, and is a two-norm operator;
the formula for obtaining the second similarity in S33 is:
Further, the formula for calculating the matching degree in S34 is as follows:
wherein ,for matching degree (I)>Is->First similarity, < >>Is->Second similarity, ++>For the number of first similarities, +.>For the second number of similarities, +.>For the first similarity constant, for statistics +.>First degree of similarityAn amount of greater than 0.5, +.>Is a second similarity constant for statistics +.>Second similarity->Greater than 0.5.
The beneficial effects of the above further scheme are: the invention compares each color distribution vector with the stored color distribution vector, calculates a plurality of first similarities, compares each eye contour feature with the stored eye contour feature, calculates a plurality of second similarities, each similarity represents the similarity of each part,similarity for counting color distribution vectors, < >>For counting the similarity of the eye contour features, +.> and />The larger the number of similar parts, the higher the similarity, and the higher the matching degree.
In summary, the invention has the following beneficial effects: according to the invention, the eye images are intercepted from the photographed face images, the eye outline features and the color distribution features are extracted from the eye images respectively, the eye outline features and the color distribution features are compared with the stored face features respectively to obtain the matching degree, and when the matching degree is higher than the matching threshold value, the face recognition is successful.
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Fig. 1 is a flowchart of a face recognition method based on eye features.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
As shown in fig. 1, a face recognition method based on eye features includes the following steps:
s1, intercepting an eye image on a face image;
in this embodiment, the screenshot eye image may adopt a neural network, and the eye position is selected through a neural network frame, so that the face image is intercepted according to the eye position selected by the frame.
The neural network has a loss function of:
wherein ,for loss function->As a logarithmic function>For loss factor->Width of eye image output for neural network, +.>Height of eye image output for neural network, +.>For the target width +.>For the target height +.>For the number of target feature pixels, +.>The number of feature pixel points in the eye image output by the neural network.
The invention sets the difference value between the ratio of the width to the height and the target ratio, thereby avoiding the problem of different heights of the eye images caused by different sizes of the imaged face images, and further considering the number of the feature pixel points in the intercepted area, the more the number of the feature pixel points is, the more the intercepted area is correct, when the number of the feature pixel points in the intercepted area is equal to the number of the target feature pixel points, the more the intercepted area is correct, the size of the intercepted area accords with the target size, and when the size of the intercepted area accords with the target size, the interception of the eye images is successful.
In this embodiment, the neural network may be a YOLO neural network.
S2, extracting eye contour features and color distribution features from the eye images respectively;
the step S2 comprises the following sub-steps:
s21, filtering the eye image to obtain a filtered image;
the filtering formula in S21 is:
wherein ,for filtering the picture->Pixel value of each pixel, +.>For the filtering factor +.>For the first part on the eye image>Pixel value of each pixel, +.>For +.>A peripheral part centered on the pixel of (2)>Pixel values of the pixel points, ||is absolute value operation, and +.>As an arctangent function.
The invention uses the characteristic of the arctangent function, takes each pixel point on the eye image as the center, discards the sliding filtering mode, and uses the characteristic of the arctangent function to obtain the sliding filtering modeWhen the difference between the pixel value and the surrounding pixel value is large, the pixel value of the surrounding pixel point is considered in the filtering process, so that the noise point can be filtered out, when the pixel value of the surrounding pixel point is large, the surrounding pixel point is also the surrounding pixel point, the characteristic that the pixel value is not influenced by the surrounding pixel point is considered, and when the difference between the pixel value and the surrounding pixel point is small, the pixel value is not influenced by the surrounding pixel point>And peripheral pixel valuesCan, without affecting the original characteristics of the spot, the filtering factor +.>For adjustable parameters, by adjusting the filter factor +.>The filtering effect can be adjusted.
S22, partitioning the filtered image to obtain a plurality of eye subareas;
s23, extracting color distribution values from each eye subarea, and constructing a color distribution matrix of each eye subarea;
the color distribution matrix in S23 is:
wherein ,is a color distribution matrix>For a first class of color distribution values of the 1 st color component in the eye subregion,for the second type of color distribution value of the 1 st color component in the eye subregion +.>A third class of color distribution values for the 1 st color component in the eye subregion>A fourth class of color distribution values for the 1 st color component in the eye subregion>Is the eye subregion->First class color component of individual color componentsCloth value (I)>Is the eye subregion->Second class of color distribution values of the individual color components, respectively>Is the eye subregion->Third class of color distribution values of the individual color components, respectively>Is the eye subregion->Fourth class of color distribution values of the individual color components, respectively>Is the eye subregion->Color distribution values of the first type of the individual color components, respectively>Is the eye subregion->Second class of color distribution values of the individual color components, respectively>Is the eye subregion->Third class of color distribution values of the individual color components, respectively>Is the eye subregion->Fourth class of color distribution values of the individual color components, respectively>Is the number of color components;
the calculation formula of the color distribution value is:
wherein ,is the eye subregion->The +.>Color component->Is the number of pixels in the ocular subregion.
The invention shows the color distribution characteristics through four types of color distribution values of a plurality of color components, thereby deeply representing the characteristics of eyes after each human eye is imaged.
S24, obtaining color distribution characteristics according to the color distribution matrix of the eye subareas;
in this embodiment, the color distribution feature is composed of a plurality of color distribution matrices.
S25, carrying out graying treatment on the filtered eye image to obtain a gray image;
s26, extracting contour features from the gray level map to obtain eye contour features.
The invention carries out filtering treatment on the eye image to filter the influence of abnormal pixel points, then carries out partition on the image, extracts color distribution values in each eye subarea, constructs a color distribution matrix for expressing the color distribution condition in one area, carries out graying treatment on the filtered eye image, extracts the outline, reduces the data quantity and simultaneously extracts effective data.
The step S26 comprises the following substeps:
s261, calculating gray characteristic values of all pixel points on a gray map, and taking the pixel points as pixel points to be contoured when the gray characteristic values are larger than a gray characteristic threshold value;
the calculation formula of the gray characteristic value in S261 is as follows:
wherein ,is the (th) on the gray level diagram>Gray characteristic value of each pixel point, +.>Is the (th) on the gray level diagram>Gray value of each pixel, +.>Is gray scaleOn the figure +.>The +.>Gray value of each pixel, +.>Is the maximum gray distance value,/>Is the minimum gray distance value,/>Is the (th) on the gray level diagram>The gray value of the 1 st pixel around the pixel,is the (th) on the gray level diagram>Gray value of 8 th pixel around each pixel, absolute value operation,/-for the 8 th pixel>For maximum value of the sequence, +.>For the minimum of the sequence.
The invention takes each pixel point on the gray level graph as the center, calculates the gray level characteristic value of each pixel point, the gray level characteristic value is used for evaluating the difference between the gray level characteristic value and the peripheral pixel points, when the gray level difference total value between the center pixel point and the peripheral pixel points is larger, the position where the gray level value is suspected to be obviously changed is indicated, therefore, the point can be a pixel point on the outline, the difference between the maximum gray level distance value and the minimum gray level distance value is found by judging the gray level distance value of the pixel point and the peripheral pixel point, if the difference between the maximum gray level distance value and the minimum gray level distance value is larger, the pixel point on the outline is further embodied, and the pixel point with the gray level characteristic value larger than the gray level characteristic threshold value is directly selected according to the gray level characteristic values of all the pixel points, so as to obtain the outline pixel point.
S262, eliminating isolated pixel points to be contoured according to the position distribution of the pixel points to be contoured to obtain eye contour feature pixel points;
s263, partitioning all the eye contour feature pixel points according to the eye contour feature pixel point positions to obtain contour sub-areas;
s264, calculating the eye contour feature of each contour subarea.
The method comprises the steps of firstly calculating the gray characteristic value of each pixel point, when the gray characteristic value is larger than a gray threshold value, determining the pixel point as a suspected outline pixel point, deleting isolated points according to whether the suspected outline pixel points are continuous in position, filtering the suspected outline pixel points, and partitioning to obtain the eye outline characteristics of the subareas.
The formula for calculating the eye contour features in S264 is:
wherein ,for eye contour features,/->Is the>Distance from each eye contour feature pixel point to the fixed eye contour feature pixel point, +.>For the number of eye contour feature pixels in the contour subregion, +.>For distance->Maximum distance in->For distance->Is the minimum distance of (a).
The fixed eye contour feature pixel point is a pixel point in the contour sub-region, which is used as a reference pixel point, the distances from other pixel points to the reference pixel point are calculated, and according to each pixel pointAnd->The difference between them shows the distance distribution and at the same time according to the maximum distance +.>And->Difference, sum->And->The difference reflects the situation among the maximum distance, the average distance and the minimum distance, and further highlights the position distribution situation of the pixel points on each contour sub-region.
S3, matching the eye contour features and the color distribution features with the stored facial image features respectively to obtain matching degree;
the stored facial image features include: a stored eye profile feature and a stored color distribution feature. The process of obtaining the stored eye profile features and the stored color distribution features is the same as the process of obtaining the eye profile features and the color distribution features described in the present invention.
The step S3 comprises the following substeps:
s31, expanding each color distribution matrix in the color distribution characteristics to form a color distribution vector;
s32, calculating the similarity between the color distribution vector and the color distribution vector in the stored facial image characteristics to obtain a first similarity;
the formula for obtaining the first similarity in S32 is as follows:
wherein ,is->First similarity, < >>Is->Color distribution vector->For store->Color distribution vector->For dot multiplication, ->Is cross-multiplied, and is a two-norm operator;
s33, calculating the similarity of the eye contour features and the eye contour features in the stored facial image features to obtain a second similarity;
the formula for obtaining the second similarity in S33 is:
S34, calculating the matching degree according to the first similarity and the second similarity.
The formula for calculating the matching degree in S34 is as follows:
wherein ,for matching degree (I)>Is->First similarity, < >>Is->Second similarity, ++>For the number of first similarities, +.>For the second number of similarities, +.>For the first similarity constant, for statistics +.>First degree of similarityAn amount of greater than 0.5, +.>Is a second similarity constant for statistics +.>Second similarity->Greater than 0.5.
The invention compares each color distribution vector with the stored color distribution vector, calculates a plurality of first similarities, compares each eye contour feature with the stored eye contour feature, calculates a plurality of second similarities, each similarity represents the similarity of each part,similarity for counting color distribution vectors, < >>For counting the similarity of the eye contour features, +.> and />The larger the similarity part is, the more the similarity is, and the matching degree isThe higher.
And S4, when the matching degree is higher than the matching threshold, the face recognition is successful.
In summary, the beneficial effects of the embodiment of the invention are as follows: according to the invention, the eye images are intercepted from the photographed face images, the eye outline features and the color distribution features are extracted from the eye images respectively, the eye outline features and the color distribution features are compared with the stored face features respectively to obtain the matching degree, and when the matching degree is higher than the matching threshold value, the face recognition is successful.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The face recognition method based on the eye features is characterized by comprising the following steps of:
s1, intercepting an eye image on a face image;
s2, extracting eye contour features and color distribution features from the eye images respectively;
s3, matching the eye contour features and the color distribution features with the stored facial image features respectively to obtain matching degree;
and S4, when the matching degree is higher than the matching threshold, the face recognition is successful.
2. The method for face recognition based on eye features according to claim 1, wherein the step S2 comprises the following sub-steps:
s21, filtering the eye image to obtain a filtered image;
s22, partitioning the filtered image to obtain a plurality of eye subareas;
s23, extracting color distribution values from each eye subarea, and constructing a color distribution matrix of each eye subarea;
s24, obtaining color distribution characteristics according to the color distribution matrix of the eye subareas;
s25, carrying out graying treatment on the filtered eye image to obtain a gray image;
s26, extracting contour features from the gray level map to obtain eye contour features.
3. The face recognition method based on the eye features according to claim 2, wherein the filtering formula in S21 is:
wherein ,for filtering the picture->Pixel value of each pixel, +.>For the filtering factor +.>For the first part on the eye image>Pixel value of each pixel, +.>For +.>A peripheral part centered on the pixel of (2)>Pixel values of the pixel points, ||is absolute value operation, and +.>As an arctangent function.
4. The method for face recognition based on eye features according to claim 2, wherein the color distribution matrix in S23 is:
wherein ,is a color distribution matrix>For the first type of color distribution value of the 1 st color component in the eye subregion +.>For the second type of color distribution value of the 1 st color component in the eye subregion +.>A third class of color distribution values for the 1 st color component in the eye subregion>A fourth class of color distribution values for the 1 st color component in the eye subregion>Is the eye subregion->Color distribution values of the first type of the individual color components, respectively>Is the eye subregion->Second class of color distribution values of the individual color components, respectively>Is the eye subregion->Third class of color distribution values of the individual color components, respectively>Is the eye subregion->Fourth class of color distribution values of the individual color components, respectively>Is the eye subregion->Color distribution values of the first type of the individual color components, respectively>Is the eye subregion->Second class of color distribution values of the individual color components, respectively>Is the eye subregion->Third class of color distribution values of the individual color components, respectively>Is the eye subregion->Fourth class of color distribution values of the individual color components, respectively>Is the number of color components;
the calculation formula of the color distribution value is:
5. The method for face recognition based on eye features according to claim 2, wherein S26 comprises the following sub-steps:
s261, calculating gray characteristic values of all pixel points on a gray map, and taking the pixel points as pixel points to be contoured when the gray characteristic values are larger than a gray characteristic threshold value;
s262, eliminating isolated pixel points to be contoured according to the position distribution of the pixel points to be contoured to obtain eye contour feature pixel points;
s263, partitioning all the eye contour feature pixel points according to the eye contour feature pixel point positions to obtain contour sub-areas;
s264, calculating the eye contour feature of each contour subarea.
6. The eye feature-based face recognition method of claim 5, wherein the calculation formula of the gray feature value in S261 is:
wherein ,is the (th) on the gray level diagram>Gray characteristic value of each pixel point, +.>Is the (th) on the gray level diagram>Gray value of each pixel, +.>Is the (th) on the gray level diagram>The +.>Gray value of each pixel, +.>For the value of the maximum gray-scale distance,is the minimum gray distance value,/>Is the (th) on the gray level diagram>The gray value of the 1 st pixel around the pixel,is the (th) on the gray level diagram>Gray value of 8 th pixel around each pixel, absolute value operation,/-for the 8 th pixel>For maximum value of the sequence, +.>To do soMinimum value of the sequence.
7. The method for face recognition based on eye features according to claim 5, wherein the formula for calculating the eye contour features in S264 is:
8. The method for face recognition based on eye features according to claim 4, wherein the step S3 comprises the following sub-steps:
s31, expanding each color distribution matrix in the color distribution characteristics to form a color distribution vector;
s32, calculating the similarity between the color distribution vector and the color distribution vector in the stored facial image characteristics to obtain a first similarity;
s33, calculating the similarity of the eye contour features and the eye contour features in the stored facial image features to obtain a second similarity;
s34, calculating the matching degree according to the first similarity and the second similarity.
9. The method for face recognition based on eye features of claim 8, wherein the formula for obtaining the first similarity in S32 is:
wherein ,is->First similarity, < >>Is->Color distribution vector->For store->Color distribution vector->For dot multiplication, ->Is cross-multiplied, and is a two-norm operator;
the formula for obtaining the second similarity in S33 is:
10. The eye feature-based face recognition method of claim 9, wherein the formula for calculating the matching degree in S34 is:
wherein ,for matching degree (I)>Is->First similarity, < >>Is->Second similarity, ++>For the number of first similarities, +.>For the second number of similarities, +.>For the first similarity constant, for statistics +.>First similarity->An amount of greater than 0.5, +.>Is a second similarity constant for statistics +.>Second similarity->Greater than 0.5.
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Citations (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20140037152A1 (en) * | 2011-04-20 | 2014-02-06 | Institute Of Automation, Chinese Academy Of Sciences | Identity recognition based on multiple feature fusion for an eye image |
CN105550671A (en) * | 2016-01-28 | 2016-05-04 | 北京麦芯科技有限公司 | Face recognition method and device |
CN106503644A (en) * | 2016-10-19 | 2017-03-15 | 西安理工大学 | Glasses attribute detection method based on edge projection and color characteristic |
US20170286754A1 (en) * | 2016-03-29 | 2017-10-05 | Microsoft Technology Licensing, Llc | Recognizing A Face And Providing Feedback On The Face-Recognition Process |
US20190050631A1 (en) * | 2016-02-26 | 2019-02-14 | Nec Corporation | Face recognition system, face recognition method, and storage medium |
WO2020258119A1 (en) * | 2019-06-27 | 2020-12-30 | 深圳市汇顶科技股份有限公司 | Face recognition method and apparatus, and electronic device |
CN114220143A (en) * | 2021-11-26 | 2022-03-22 | 华南理工大学 | Face recognition method for wearing mask |
US20220343680A1 (en) * | 2021-05-25 | 2022-10-27 | Beijing Baidu Netcom Scinece Technology Co., Ltd. | Method for face liveness detection, electronic device and storage medium |
CN115953823A (en) * | 2023-03-13 | 2023-04-11 | 成都运荔枝科技有限公司 | Face recognition method based on big data |
-
2023
- 2023-05-30 CN CN202310618256.8A patent/CN116343313B/en active Active
Patent Citations (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20140037152A1 (en) * | 2011-04-20 | 2014-02-06 | Institute Of Automation, Chinese Academy Of Sciences | Identity recognition based on multiple feature fusion for an eye image |
CN105550671A (en) * | 2016-01-28 | 2016-05-04 | 北京麦芯科技有限公司 | Face recognition method and device |
US20190050631A1 (en) * | 2016-02-26 | 2019-02-14 | Nec Corporation | Face recognition system, face recognition method, and storage medium |
US20170286754A1 (en) * | 2016-03-29 | 2017-10-05 | Microsoft Technology Licensing, Llc | Recognizing A Face And Providing Feedback On The Face-Recognition Process |
CN106503644A (en) * | 2016-10-19 | 2017-03-15 | 西安理工大学 | Glasses attribute detection method based on edge projection and color characteristic |
WO2020258119A1 (en) * | 2019-06-27 | 2020-12-30 | 深圳市汇顶科技股份有限公司 | Face recognition method and apparatus, and electronic device |
US20220343680A1 (en) * | 2021-05-25 | 2022-10-27 | Beijing Baidu Netcom Scinece Technology Co., Ltd. | Method for face liveness detection, electronic device and storage medium |
CN114220143A (en) * | 2021-11-26 | 2022-03-22 | 华南理工大学 | Face recognition method for wearing mask |
CN115953823A (en) * | 2023-03-13 | 2023-04-11 | 成都运荔枝科技有限公司 | Face recognition method based on big data |
Non-Patent Citations (3)
Title |
---|
廖艺捷;刘昊阳;崔文豪;: "基于积分投影改进的人脸识别模式", 信息与电脑(理论版), no. 15, pages 127 - 130 * |
赵广谱等: "基于肤色阈值分割的动静态结合的人眼状态识别方法", 《第22届中国***仿真技术及其应用学术年会论文集》, pages 416 - 421 * |
陈莉明: "基于积分投影改进的人脸识别模式", 《华中科技大学学报(自然科学版)》, no. 2018, pages 127 - 130 * |
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