Robust precise eye location under probabilistic framework

Eye feature location is an important step in automatic visual interpretation and human face recognition. In this paper, a novel approach for locating eye centers in face areas under probabilistic framework is devised. After grossly locating a face, we first find the areas which left and right eyes lies in. Then an appearance-based eye detector is used to detect the possible left and right eye separately. According to their probabilities, the candidates are subsampled to merge those in near positions. Finally, the remaining left and right eye candidates are paired; each possible eye pair is normalized and verified. According to their probabilities, the precise eye positions are decided. The experimental results demonstrate that our method can effectively cope with different eye variations and achieve better location performance on diverse test sets than some newly proposed methods. And the influence of precision of eye location on face recognition is also probed. The location of other face organs such as mouth and nose can be incorporated in the framework easily.

[1]  Zhiwei Zhu,et al.  Real-time eye detection and tracking under various light conditions , 2002, ETRA.

[2]  Sridha Sridharan,et al.  Improved Facial-Feature Detection for AVSP via Unsupervised Clustering and Discriminant Analysis , 2003, EURASIP J. Adv. Signal Process..

[3]  Takeo Kanade,et al.  A statistical approach to 3d object detection applied to faces and cars , 2000 .

[4]  Volkan Atalay,et al.  Projection based method for segmentation of human face and its evaluation , 2002, Pattern Recognit. Lett..

[5]  Fabrizio Smeraldi,et al.  Retinal vision applied to facial features detection and face authentication , 2002, Pattern Recognit. Lett..

[6]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[7]  Chengjun Liu,et al.  A Gabor feature classifier for face recognition , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[8]  Yehezkel Yeshurun,et al.  Context-free attentional operators: The generalized symmetry transform , 1995, International Journal of Computer Vision.

[9]  Takeo Kanade,et al.  A statistical method for 3D object detection applied to faces and cars , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[10]  C. Keating,et al.  Monkeys and mug shots: cues used by rhesus monkeys (Macaca mulatta) to recognize a human face. , 1993, Journal of comparative psychology.

[11]  Roberto Brunelli,et al.  Face Recognition: Features Versus Templates , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Josef Kittler,et al.  Hypotheses-Driven Affine Invariant Localization of Faces in Verification Systems , 2003, AVBPA.

[13]  David,et al.  Pose Discriminiation and Eye Detection Using Support Vector Machines (SVM) , 1998 .

[14]  Zhi-Hua Zhou,et al.  Projection functions for eye detection , 2004, Pattern Recognit..

[15]  Pong C. Yuen,et al.  Multi-cues eye detection on gray intensity image , 2001, Pattern Recognit..

[16]  Harry Wechsler,et al.  Face pose discrimination using support vector machines (SVM) , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[17]  Mohamed Rizon,et al.  Detection of eyes from human faces by Hough transform and separability filter , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[18]  Y. Freund,et al.  Discussion of the Paper \additive Logistic Regression: a Statistical View of Boosting" By , 2000 .

[19]  Zhuowen Tu,et al.  Image Parsing: Unifying Segmentation, Detection, and Recognition , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[20]  Irfan A. Essa,et al.  Detecting and tracking eyes by using their physiological properties, dynamics, and appearance , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[21]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[22]  Keating Cf,et al.  Monkeys and mug shots: cues used by rhesus monkeys (Macaca mulatta) to recognize a human face , 1993 .