CN101984453A - Human eye recognition system and method - Google Patents
Human eye recognition system and method Download PDFInfo
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- CN101984453A CN101984453A CN201010531501.4A CN201010531501A CN101984453A CN 101984453 A CN101984453 A CN 101984453A CN 201010531501 A CN201010531501 A CN 201010531501A CN 101984453 A CN101984453 A CN 101984453A
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Abstract
The invention discloses a human eye recognition system and method. The system comprises an identification module of a human eye area to be detected, a human eye detection module and a human eye weight recognition module, wherein the identification module of the human eye area to be detected is used for marking off the area to be detected of a left eye and a right eye on a human face area; the human eye detection module is used for detecting the target area of the left eye and the right eye from the area to be detected of a left eye and a right eye; the human eye weight recognition module is used for the weight calculation to the target area of the left eye and the right eye to obtain the human eye area; and the weight comprises the approximate degree of the size of the left eye and the right eye, the reasonable degree of the ratio of side length of the left eye area and the right eye area to the space between a left eye pupil and a right eye pupil, and an included angle between the ligature of the space between a left eye pupil and a right eye pupil and the horizontal direction. The human eye recognition system of the invention precisely positions the human eyes by three weights after detecting the target area of the left eye and the right eye, effectively solves the problem of positioning the target area of the human eyes and improves the accuracy of human eye recognition.
Description
Technical field
The present invention relates generally to the living things feature recognition field, specifically, relate to a kind of auxiliary human eye detection system and method based on eyes structure weights.
Background technology
Development along with computer vision technique, recognition of face and Expression Recognition more and more come into one's own, and human eye detection is as the priori conditions of the pre-treatment step of recognition of face and Expression Recognition, its accuracy of detection and speed directly have influence on the precision and the speed of identification, and the accuracy of human eye detection can improve recognition of face and Expression Recognition accuracy and automaticity effectively.At present, most Expression Recognition and face recognition algorithms all are the human eyes of having good positioning earlier, carry out the calculating of normalization, feature calculation and unique point then according to the positional information of human eye, and then carry out Expression Recognition or recognition of face.
With reference to figure 1, Fig. 1 shows the method flow diagram of at present main human eye identification, and its key step comprises: gather the picture pedestrian's face of going forward side by side and detect, obtain people's face rectangular area; With the rectangular area, draw and get left eye and right eye zone to be detected; In extracted region Haar feature to be detected (simple rectangular characteristic), and collect non-human eye area and inhuman eye pattern sheet, extract the Haar feature; According to the Haar feature of human eye picture and inhuman eye pattern sheet, training AdaBoost human eye detection model; The test picture obtains left eye and right eye zone to be detected, calculates the Haar feature, input AdaBoost human eye detection model; If about the zone detect a human eye respectively, algorithm success, otherwise failure.Existing human eye location algorithm, though obtained certain effect, but still have problems, such as locating out of true, can't locating a plurality of human eyes, so many times still need a large amount of manual correction in the human eye position fixing process, this problem causes being difficult to realize the robotization of Expression Recognition and recognition of face.
Therefore, be necessary to propose a kind of recognition system and the method that can accurately locate human eye.
Summary of the invention
In order to address the above problem, the invention provides a kind of human eye recognition system, described system comprises: human eye regional identification module to be measured is used for drawing from human face region and gets left eye and right eye zone to be detected; The human eye detection module is used for detecting left eye and right eye target area from left eye and right eye zone to be detected; Human eye weights identification module, be used for calculating human eye area by weights are carried out in left eye and right eye target area, described weights comprise resonable degree and the right and left eyes interocular distance line and the horizontal direction angle of big or small degree of closeness, the right and left eyes zone length of side and the right and left eyes interocular distance ratio of right and left eyes.
The present invention also provides a kind of human eye recognition methods, and described method comprises: obtain human face region, draw from described human face region and get left eye and right eye zone to be detected; Detect left eye and right eye target area from left eye and right eye zone to be detected; Weights are carried out in left eye and right eye target area calculate human eye area, described weights comprise resonable degree and the right and left eyes interocular distance line and the horizontal direction angle of big or small degree of closeness, the right and left eyes zone length of side and the right and left eyes interocular distance ratio of right and left eyes.
By adopting human eye recognition system of the present invention, after detecting left eye and right eye target area, the resonable degree of the big or small degree of closeness by right and left eyes, the right and left eyes zone length of side and right and left eyes interocular distance ratio and right and left eyes interocular distance line and these three weights of horizontal direction angle are located human eye, effectively solve the orientation problem of a plurality of human eyes target area, improved the accuracy and the automaticity of human eye identification.
Description of drawings
Fig. 1 shows the process flow diagram of human eye recognition system;
Fig. 2 shows the synoptic diagram of human eye detection Haar feature;
Fig. 3 shows human eye recognition system structural representation and the process flow diagram according to the embodiment of the invention;
Fig. 4, Fig. 5 show the human eye sample area synoptic diagram according to the embodiment of the invention.
Embodiment
Describe embodiments of the invention below in detail, the example of described embodiment is shown in the drawings, is exemplary below by the embodiment that is described with reference to the drawings, and only is used to explain the present invention, and can not be interpreted as limitation of the present invention.
With reference to figure 3, the human eye recognition system of the embodiment of the invention comprises: human eye regional identification module 100 to be measured, human eye detection module 200 and human eye weights identification module 300.
Described human eye regional identification module 100 to be measured is used for drawing from human face region and gets left eye and right eye zone to be detected.
Described human eye detection module 200 is used for detecting left eye and right eye target area from left eye and right eye zone to be detected.In embodiments of the present invention, described human eye detection module comprises human eye feature extraction module and human eye identification module, the feature that is used to extract left eye and right eye zone to be detected by described human eye feature extraction module, be used for getting rid of the non-human eye area of feature of described extraction by described human eye identification module, to obtain left eye and right eye target area.
Described human eye weights identification module 300, be used for calculating human eye area by weights are carried out in left eye and right eye target area, described weights comprise resonable degree and the right and left eyes interocular distance line and the horizontal direction angle of big or small degree of closeness, the right and left eyes zone length of side and the right and left eyes interocular distance ratio of right and left eyes.
According to process flow diagram shown in Figure 3, can realize the human eye identification of described human eye recognition system by following step and method.
At step S01,, obtain people's face rectangular area by detecting at the enterprising pedestrian's face of the portrait picture of gathering.
At step S02, draw from human face region and to get left eye and right eye zone to be detected.
At step S03, extract the feature in left eye and right eye zone to be detected.In embodiments of the present invention, can be by extracting the feature of Haar feature as identification people's face and human eye, the Haar feature is divided three classes usually: edge feature, linear feature, central feature and diagonal line feature, be combined into feature templates, as shown in Figure 2, adularescent and two kinds of rectangles of black in the feature templates, and can be by the eigenwert that defines this template the white rectangle pixel and deduct the black rectangle pixel and, therefore can distinguish human eye and non-human eye by on the Haar of picture characteristic set, selecting suitable feature.
At step S04, get rid of non-human eye area in the feature of described extraction, to obtain left eye and right eye target area.In embodiments of the present invention, can pass through the AdaBoost algorithm, adopt wipe-out mode to remove non-human eye area, obtain left eye and right eye target area based on the Haar characteristic information.The AdaBoost algorithm is a kind of method of cascade sort, and it lumps together some more weak sorting techniques, the very strong sorting technique that combination makes new advances.In embodiments of the present invention, at first, can from the Haar feature, select effective Haar feature by the AdaBoost algorithm and train the right and left eyes detecting device.Training sample is divided into positive example sample and counter-example sample, wherein the positive example sample is meant target sample to be checked (left eye and right eye), the counter-example sample refers to other any image, all samples pictures all are normalized to same size, size in samples pictures described in the one embodiment of the invention can be 20*20, the size of described samples pictures can come to determine as required, can also be 30*30 or other suitable dimensions.After sorter has been trained, can be applied to the detection in the zone to be detected of left eye in the input picture and right eye, described zone to be detected has identical size with training sample.In the image detection process, with image convergent-divergent, the subwindow of exhaustive each 20*20 of the picture behind convergent-divergent then successively in proportion.By each grade sorter, most candidate region just has been excluded window in which floor the detection so in front successively, and all the zone of detecting by each grade sorter is the target area.Then, adopt wipe-out mode to remove non-human eye area based on the Haar characteristic information, what testing process finished to obtain is all zones that are not excluded.But in the bad situation of light, zone such as canthus eyebrow and human eye have similar Haar feature, are easy to be identified as human eye by mistake, the situation that people's face one side identifies a plurality of human eyes occurs.
At step S05, weights are carried out in left eye and right eye target area calculate human eye area, the present invention calculates accurate human eye area by three weights, described three weights comprise big or small degree of closeness, the right and left eyes zone length of side of right and left eyes and resonable degree and the right and left eyes interocular distance line and the horizontal direction angle of right and left eyes interocular distance ratio, by judge two human eye degrees of closeness, eyes size and interpupillary distance ratio whether near 0.6 and interpupillary line and horizontal sextant angle obtain a human eye area the most accurate near these three weights of level.In the embodiment of the invention, described weight calculation method is:
The big or small degree of closeness of described left eye and right eye: W
1=1-|S
l-S
r|/(S
l+ S
r), S
lRepresent the left eye region length of side, S
rRepresent the right eye region length of side.If S
lAnd S
rSize is approaching so | S
l-S
r| near 0, thus W
1Approach 1, therefore, for the big or small degree of closeness W of described left eye and right eye
1Value approach 1 more, the size of left eye and right eye is approaching more.
The length of side in described right and left eyes zone and the resonable degree of right and left eyes interocular distance ratio: W
2=1-|L
p-L|/(L
p+ L), L
pRepresent the spacing of two pupils, L is by L=(S
l+ S
r) * 5/6 calculates and get, in embodiments of the present invention, than whether judges described weights W near 0.6 by the regional length of side of right and left eyes with the right and left eyes interocular distance
2Resonable degree, L wherein
pRepresent the distance of two pupil actual measurements, L is to be the reference value that the empirical value of interocular distance 0.6 calculates by the right and left eyes length of side, with reference to figure 4, L
pApproaching more with L, W
2Value approach 1 more, the length of side in described right and left eyes zone and right and left eyes interocular distance think then that than more reasonable right and left eyes more meets normal eye's standard.
The angle of described right and left eyes pupil center's line and horizontal direction: W
3=D
x/ (D
x+ D
y), D
xRepresent the horizontal range of two pupils; D
yRepresent the vertical range of two pupils, with reference to figure 5, if people's eye coordinates (x of pair of right and left human eye
l, y
l) and (x
r, y
r) then the difference in height of two eyes be D
x, D
y, be in normal condition as if human eye, then D
yApproach 0, described weights W
3Approach 1, described weights W
3Be 1 to the maximum.
For above each weights, too small irrational data appear sometimes, can as required it be screened out, for example, keep data, give less negative value too small data near 1, for example-10, when in the end weights are selected, with the data of not considering about-10.
For better explanation the present invention, below will be elaborated with embodiment based on the frightened Expression Recognition of head movement.Described embodiment need set up an expression database in advance, and each expression comprises start frame and exaggerates frame most in the database, and the equal mark of the expression in the database.
In the identifying, at first, take out all expression start frames and exaggerate frame most, advanced pedestrian's face detects in every width of cloth picture, obtains detected rectangle human face region, and the height of rectangular area: height is wide: width.
Then, determine the human eye target area.Get top left region: (x ∈ [0, width/2], y ∈ [0,0.6*height]) from the human eye detection algorithm of top left region application based on Haar feature and AdaBoost sorter, detects M human eye; In like manner, use identical algorithms, detect N human eye in right regions (x ∈ [width/2, width], y ∈ [0,0.6*height]).
Then, determine accurate human eye area.If M*N=0; The failure of human eye detection algorithm; If M*N=1; The success of human eye detection algorithm; If M*N＞1, then all detected human eyes are formed M*N to human eye; According to the detected every pair of human eye calculate three weights and.Weighting value and maximum human eye are to as net result.The success of human eye detection algorithm.
According to detected human eye, calculate the head movement information characteristics.Adopt sorter that feature is classified, and then whether identification is frightened expression.
More than human eye recognition system of the present invention and method are described in detail, the resonable degree of the big or small degree of closeness by right and left eyes, the right and left eyes zone length of side and right and left eyes interocular distance ratio and right and left eyes interocular distance line and these three weights of horizontal direction angle are located human eye, effectively solve the orientation problem of a plurality of human eyes target area, improved the accuracy and the automaticity of human eye identification.
One of ordinary skill in the art will appreciate that and realize that all or part of step that the foregoing description method is carried is to instruct relevant hardware to finish by program, described program can be stored in a kind of computer-readable recording medium, this program comprises one of step or its combination of method embodiment when carrying out.
In addition, each functional unit in each embodiment of the present invention can be integrated in the processing module, also can be that the independent physics in each unit exists, and also can be integrated in the module two or more unit.Above-mentioned integrated module both can adopt the form of hardware to realize, also can adopt the form of software function module to realize.If described integrated module realizes with the form of software function module and during as independently production marketing or use, also can be stored in the computer read/write memory medium.
The above-mentioned storage medium of mentioning can be a ROM (read-only memory), disk or CD etc.
The above only is embodiments of the present invention; should be pointed out that for those skilled in the art, under the prerequisite that does not break away from the principle of the invention; can also make some improvements and modifications, these improvements and modifications also should be considered as protection scope of the present invention.
Claims (10)
1. human eye recognition system, described system comprises:
Human eye regional identification module to be measured is used for drawing from human face region and gets left eye and right eye zone to be detected;
The human eye detection module is used for detecting left eye and right eye target area from left eye and right eye zone to be detected;
Human eye weights identification module, be used for calculating human eye area by weights are carried out in left eye and right eye target area, described weights comprise resonable degree and the right and left eyes interocular distance line and the horizontal direction angle of big or small degree of closeness, the right and left eyes zone length of side and the right and left eyes interocular distance ratio of right and left eyes.
2. system according to claim 1, it is characterized in that, described human eye detection module comprises human eye feature extraction module and human eye identification module, described human eye feature extraction module is used to extract the Haar feature in left eye and right eye zone to be detected, described human eye identification module is used for getting rid of the non-human eye area of Haar feature of described extraction, to obtain left eye and right eye target area.
3. system according to claim 2 is characterized in that, described human eye identification module detects identification object region by the AdaBoost algorithm.
4. system according to claim 1 is characterized in that, the big or small degree of closeness W of described right and left eyes
1=1-|S
l-S
r|/(S
l+ S
r), S
lRepresent the left eye region length of side, S
rRepresent the right eye region length of side; The resonable degree W of the right and left eyes zone length of side and right and left eyes interocular distance ratio
2=1-|L
p-L|/(L
p+ L), L
pRepresent the spacing of two pupils, L=(S
l+ S
r) * 5/6; Right and left eyes interocular distance line and horizontal direction angle, wherein W
3=D
x/ (D
x+ D
y), D
xRepresent the horizontal range of two pupils, D
yRepresent the vertical range of two pupils.
5. system according to claim 1 is characterized in that, by described weights addition is obtained weights and, weights and maximum human eye target area are defined as human eye area.
6. human eye recognition methods, described method comprises:
Obtain human face region, draw from described human face region and get left eye and right eye zone to be detected;
Detect left eye and right eye target area from left eye and right eye zone to be detected;
Weights are carried out in left eye and right eye target area calculate human eye area, described weights comprise resonable degree and the right and left eyes interocular distance line and the horizontal direction angle of big or small degree of closeness, the right and left eyes zone length of side and the right and left eyes interocular distance ratio of right and left eyes.
7. method according to claim 6 is characterized in that, the method that obtains described left eye and right eye target area comprises: the Haar feature of extracting left eye and right eye zone to be detected; Get rid of non-human eye area in the Haar feature of described extraction, to obtain left eye and right eye target area.
8. method according to claim 7 is characterized in that, the method for getting rid of non-human eye area in the Haar feature of described extraction is the AdaBoost classification.
9. method according to claim 6 is characterized in that, the big or small degree of closeness W of described right and left eyes
1=1-|S
l-S
r|/(S
l+ S
r), S
lRepresent the left eye region length of side, S
rRepresent the right eye region length of side; The resonable degree W of the right and left eyes zone length of side and right and left eyes interocular distance ratio
2=1-|L
p-L|/(L
p+ L), L
pRepresent the spacing of two pupils, L=(S
l+ S
r) * 5/6; Right and left eyes interocular distance line and horizontal direction angle W
3=D
x/ (D
x+ D
y), D
xRepresent the horizontal range of two pupils, D
yRepresent the vertical range of two pupils.
10. method according to claim 6 is characterized in that, by described weights addition is obtained weights and, weights and maximum human eye target area are defined as human eye area.
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| CN102426644A (en) * | 2011-08-24 | 2012-04-25 | 夏东 | Human eye cross localization method based on GentleBoost machine learning |
| CN102830797A (en) * | 2012-07-26 | 2012-12-19 | 深圳先进技术研究院 | Man-machine interaction method and system based on sight judgment |
| CN102867172A (en) * | 2012-08-27 | 2013-01-09 | Tcl集团股份有限公司 | Human eye positioning method, system and electronic equipment |
| CN104346621A (en) * | 2013-07-30 | 2015-02-11 | 展讯通信(天津)有限公司 | Method and device for creating eye template as well as method and device for detecting eye state |
| CN104679225A (en) * | 2013-11-28 | 2015-06-03 | 上海斐讯数据通信技术有限公司 | Method and device for adjusting screen of mobile terminal and mobile terminal |
| CN104732202A (en) * | 2015-02-12 | 2015-06-24 | 杭州电子科技大学 | Method for eliminating influence of glasses frame during human eye detection |
| CN104933344A (en) * | 2015-07-06 | 2015-09-23 | 北京中科虹霸科技有限公司 | Mobile terminal user identity authentication device and method based on multiple biological feature modals |
| CN109118626A (en) * | 2018-08-08 | 2019-01-01 | 腾讯科技（深圳）有限公司 | Control method, device, storage medium and the electronic device of lockset |
| WO2019033569A1 (en) * | 2017-08-17 | 2019-02-21 | 平安科技（深圳）有限公司 | Eyeball movement analysis method, device and storage medium |
| US20200337556A1 (en) * | 2017-12-13 | 2020-10-29 | Medical Diagnostech Pty Ltd. | System and method for obtaining a pupil response profile |
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| CN102426644A (en) * | 2011-08-24 | 2012-04-25 | 夏东 | Human eye cross localization method based on GentleBoost machine learning |
| CN102830797B (en) * | 2012-07-26 | 2015-11-25 | 深圳先进技术研究院 | A kind of man-machine interaction method based on sight line judgement and system |
| CN102830797A (en) * | 2012-07-26 | 2012-12-19 | 深圳先进技术研究院 | Man-machine interaction method and system based on sight judgment |
| CN102867172A (en) * | 2012-08-27 | 2013-01-09 | Tcl集团股份有限公司 | Human eye positioning method, system and electronic equipment |
| CN104346621A (en) * | 2013-07-30 | 2015-02-11 | 展讯通信(天津)有限公司 | Method and device for creating eye template as well as method and device for detecting eye state |
| CN104679225B (en) * | 2013-11-28 | 2018-02-02 | 上海斐讯数据通信技术有限公司 | Screen adjustment method, screen adjustment device and the mobile terminal of mobile terminal |
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| CN104732202A (en) * | 2015-02-12 | 2015-06-24 | 杭州电子科技大学 | Method for eliminating influence of glasses frame during human eye detection |
| CN104933344A (en) * | 2015-07-06 | 2015-09-23 | 北京中科虹霸科技有限公司 | Mobile terminal user identity authentication device and method based on multiple biological feature modals |
| CN104933344B (en) * | 2015-07-06 | 2019-01-04 | 北京中科虹霸科技有限公司 | Mobile terminal user identity authentication device and method based on multi-biological characteristic mode |
| WO2019033569A1 (en) * | 2017-08-17 | 2019-02-21 | 平安科技（深圳）有限公司 | Eyeball movement analysis method, device and storage medium |
| US10534957B2 (en) | 2017-08-17 | 2020-01-14 | Ping An Technology (Shenzhen) Co., Ltd. | Eyeball movement analysis method and device, and storage medium |
| US20200337556A1 (en) * | 2017-12-13 | 2020-10-29 | Medical Diagnostech Pty Ltd. | System and method for obtaining a pupil response profile |
| US11690509B2 (en) * | 2017-12-13 | 2023-07-04 | Medical Diagnostech Pty Ltd. | System and method for obtaining a pupil response profile |
| CN109118626A (en) * | 2018-08-08 | 2019-01-01 | 腾讯科技（深圳）有限公司 | Control method, device, storage medium and the electronic device of lockset |
| CN109118626B (en) * | 2018-08-08 | 2022-09-13 | 腾讯科技（深圳）有限公司 | Lock control method and device, storage medium and electronic device |
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