Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Detection Using a Maximal Rejection Classifier
IWVF-4 Proceedings of the 4th International Workshop on Visual Form
Recursive neural networks learn to localize faces
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Contour extraction of facial feature components using template based snake algorithm
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
Applying biometric principles to avatar recognition
Transactions on computational science XII
Robotics and Autonomous Systems
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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 using 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.