EYE CENTER LOCALIZATION ON A FACIAL IMAGE BASED ON MULTI-BLOCK LOCAL BINARY PATTERNS

Transcription

1 P.G. Demdov Yaroslavl State Unversty Anatoly Ntn, Vladmr Khryashchev, Olga Stepanova, Igor Kostern EYE CENTER LOCALIZATION ON A FACIAL IMAGE BASED ON MULTI-BLOCK LOCAL BINARY PATTERNS Yaroslavl, 2015

2 Eye localzaton tas Face detecton Eye localzaton We address the tas of accurately localzng the eyes n face mages extracted by a face detector 2

3 Challenges of eye localzaton wde varety of colors and shapes of eyes emotons and facal expressons occlusons pose magng condton and qualty 3

4 Approaches n eye localzaton measurng eye characterstcs learnng statstcal appearance model explotng the spatal structure of the face descrbng the local structure of the mage 4

5 Local Bnary Patterns Ths operator s defned for each pxel by thresholdng the 3 3 neghborhood pxel value wth the center pxel value. In ths way, t can gve us a bnary sequence defned by local structure of an mage LBP are computatonally effcent nvarant to changes n the brghtness of the mage caused by shootng n dfferent lghtng condtons T. Ojala, M. Petänen, and D. Harwood. A Comparatve Study of Texture Measures wth Classfcaton Based on Feature Dstrbutons // Pattern Recognton, vol. 29, 1996, pp

6 Mult-Bloc Local Bnary Patterns Integral mage s used for rapd computaton of MB-LBP operators. L. Zhang, R.Chu, S. Xang, S. Lao, S. Z. L Face Detecton Based on Mult-Bloc LBP Representaton, Advances n Bometrcs, Lecture Notes n Computer Scence, Volume 642, 2007, pp

7 Mult-Bloc Local Bnary Patterns For resoluton 8х6: S = 45 features 21х15: S = 2450 features 21х21: S = 4900 features 7

8 The eye tranng samples Tranng dataset The start weghts should satsfy the followng condton 8

9 Classfcaton stage Ideal classfer: It s constructed as a superposton of wea classfers: F( x) F( x) 1, 1, sgn( for for T t1 f t xe xe ( x)) x ( x 1,.., x,.., x S x [0;255] ) S s the number of MB-LBP values for chosen resoluton 9

10 Constructng wea classfers 10 Wea classfers are constructed as mult branch decson trees: 256.,...,... 1, ),,,, ( ) ( j S x если a j x если a x если a x x x f f x N N j j x w j x w y a ) ( ) (, f the number of eye samples wth approprate feature value s bgger than number of negatve eye patterns 0 a j

11 In step t, the wea classfer weghted squared error: f t Gentle AdaBoost F N ( x) mn w ( y f ( x f 1 The weghts are updated : w ' w f t e (x) y f t ( x s chosen so as to mnmze the If the sample s classfed ncorrectly at ths step the weght for ths sample becomes bgger. ) 2 )) Schapre R.; Snger Y. Improved Boostng Algorthms Usng Confdence-rated Predctons // Machne Learnng, vol. 37, ssue 3, 1999, pp

12 Adaptve Eye Localzer T F( x) ( x) t1 f t F (x) F (x) F(x) 12

13 13

14 Evaluaton The FERET database: 3,363 mages MB-LBP and Bayesan algorthms are traned on 1000 of 3,363 mages whle tranng for gradent detector s not requred. Of the rest 2,363 frontal mages, the 2,350 mages for whch the face detector detected a face correctly were retaned for testng. The BoID database: 1,521 mages Of the 1,521 mages, the 1,469 mages for whch the face detector detected a face correctly were used for testng. 14

15 Evaluaton err max( l l l g g, r g r r g ) 15

16 Expermental results: FERET 1. Bayesan approach: M. R. Everngham and A. Zsserman. Regresson and classfcaton approaches to eye localzaton n face mages // IEEE Internatonal Conference on Automatc Face & Gesture Recognton, 2006, pp Gradent approach: Tmm F., Barth E. Accurate Eye Centre Localsaton by Means of Gradents // 6th Internatonal Conf. Computer Vson Theory and Applcatons,

17 Expermental results: BoID п 17

18 Tme costs Algorthm Gradent Bayesan MB-LBP Tme of processng 587 ms 367 ms 44 ms 18

19 Vsual examples 19

20 P.G. Demdov Yaroslavl State Unversty Anatoly Ntn, Vladmr Khryashchev, Olga Stepanova EYE CENTER LOCALIZATION ON A FACIAL IMAGE BASED ON MULTI-BLOCK LOCAL BINARY PATTERNS Yaroslavl, 2015