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

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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

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

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

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

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. 51-59. 5

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. 11-18. 6

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

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

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

Constructng wea classfers 10 Wea classfers are constructed as mult branch decson trees: 256.,...,... 1, ),,,, ( ) ( 256 1 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

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. 297-336. 11

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

13

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

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

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. 441-446. 2. Gradent approach: Tmm F., Barth E. Accurate Eye Centre Localsaton by Means of Gradents // 6th Internatonal Conf. Computer Vson Theory and Applcatons, 2011. 16

Expermental results: BoID п 17

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

Vsual examples 19

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

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