Finding faces in photographs

Two new schemes are presented for finding human faces in a photograph. The first scheme approximates the unknown distributions of the face and the face-like manifolds wing higher order statistics (HOS). An HOS-based data clustering algorithm is also proposed. In the second scheme, the face to non-face and non-face to face transitions are learnt using a hidden Markov model (HMM). The HMM parameters are estimated corresponding to a given photograph and the faces are located by examining the optimal state sequence of the HMM. Experimental results are presented on the performance of both the schemes.

[1]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[2]  Jr. G. Forney,et al.  The viterbi algorithm , 1973 .

[3]  L. Rabiner,et al.  An introduction to hidden Markov models , 1986, IEEE ASSP Magazine.

[4]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[5]  Peter Seitz,et al.  Using local orientation and hierarchical spatial feature matching for the robust recognition of objects , 1991, Other Conferences.

[6]  Thomas S. Huang,et al.  Human face detection in a scene , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Steve J. Young,et al.  HMM-based architecture for face identification , 1994, Image Vis. Comput..

[8]  Tomaso A. Poggio,et al.  Finding Human Faces with a Gaussian Mixture Distribution-Based Face Model , 1995, ACCV.

[9]  Alex Pentland,et al.  Probabilistic visual learning for object detection , 1995, Proceedings of IEEE International Conference on Computer Vision.

[10]  Takeo Kanade,et al.  Neural network-based face detection , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.