Viewpoint Invariant Face Detection

In this paper we present a face model based on learning a relation between local features and a face invariant. We have developed a face invariant model for accurate face localization in natural images that is robust to viewpoints changes. A probabilistic model learned from a training set captures a relationship between features appearance and face invariant geometry. It is then used to infer a face instance in new image. We use the invariant local features which have the high performances of objects appearance distinctiveness. The face appearance features are recognized by EM classification. Then, face invariant parameters are predicted and a hierarchical clustering method achieves invariant geometric localization. The clustering uses an aggregate value to construct clusters of invariants. The face appearance probabilities of features are computed to select the best clusters and thus to localize faces in images. We evaluate our generic invariant by testing it in face detection experiments on PIE, FERET and CMU-Profiles databases. The experimental results show that our face invariant model gives highly accurate face localization.

[1]  Pietro Perona,et al.  Object class recognition by unsupervised scale-invariant learning , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[2]  Pietro Perona,et al.  A Bayesian approach to unsupervised one-shot learning of object categories , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[3]  Michael Brady,et al.  Saliency, Scale and Image Description , 2001, International Journal of Computer Vision.

[4]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression (PIE) database , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[5]  Dan Roth,et al.  Learning to detect objects in images via a sparse, part-based representation , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Jean-Michel Morel,et al.  A fully affine invariant image comparison method , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[7]  Matthew Toews,et al.  Detection Over Viewpoint via the Object Class Invariant , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[8]  Antti Oulasvirta,et al.  Computer Vision – ECCV 2006 , 2006, Lecture Notes in Computer Science.

[9]  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.

[10]  J.B. Burns,et al.  View Variation of Point-Set and Line-Segment Features , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[12]  David G. Lowe,et al.  Probabilistic Models of Appearance for 3-D Object Recognition , 2000, International Journal of Computer Vision.

[13]  Jiří Matas,et al.  Computer Vision - ECCV 2004 , 2004, Lecture Notes in Computer Science.

[14]  Cordelia Schmid,et al.  Selection of scale-invariant parts for object class recognition , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[15]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[16]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[17]  Shimon Ullman,et al.  View-Invariant Recognition Using Corresponding Object Fragments , 2004, ECCV.