A bottom-up framework for robust facial feature detection

Registration of facial features is a significant step towards a complete solution of the face recognition problem. We have built a general framework for detecting a set of individual facial features such as eyes, nose and lips using a bottom-up approach. A joint model of discriminative and generative learners is employed providing unprecedented results in terms of both detection rate and false positives rate. An Adaboost cascade learner is used to find candidates for facial features and a graphical model selects the most likely combination of features based on their individual likelihoods as well as relative positions and infers the missing components. We show good detection results on different large image datasets under challenging imaging conditions.

[1]  William T. Freeman,et al.  Nonparametric belief propagation , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[2]  Yajie Tian,et al.  Handbook of face recognition , 2003 .

[3]  Timothy F. Cootes,et al.  Statistical models of appearance for computer vision , 1999 .

[4]  Ian R. Fasel,et al.  A generative framework for real time object detection and classification , 2005, Comput. Vis. Image Underst..

[5]  David Beymer,et al.  Eye gaze tracking using an active stereo head , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[6]  Timothy F. Cootes,et al.  Active Appearance Models , 1998, ECCV.

[7]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[8]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Thomas Serre,et al.  Categorization by Learning and Combining Object Parts , 2001, NIPS.

[10]  Martial Hebert,et al.  Discriminative random fields: a discriminative framework for contextual interaction in classification , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[11]  Hyeonjoon Moon,et al.  The FERET evaluation methodology for face-recognition algorithms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Zhenyun Peng,et al.  Detecting Facial Features on Images with Multiple Faces , 2000, ICMI.

[13]  David J. Spiegelhalter,et al.  Probabilistic Networks and Expert Systems , 1999, Information Science and Statistics.

[14]  Paul A. Viola,et al.  Robust Real-time Object Detection , 2001 .

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

[16]  Andrew McCallum,et al.  Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data , 2001, ICML.