Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminant Analysis of Principal Components for Face Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Hybrid Hidden Markov Model for Face Recognition
SSIAI '00 Proceedings of the 4th IEEE Southwest Symposium on Image Analysis and Interpretation
Face Database Retrieval Using Pseudo 2D Hidden Markov Models
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Discriminant Analysis of Stochastic Models and Its Application to Face Recognition
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Face recognition using discriminant eigenvectors
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 04
Component-based discriminative classification for hidden Markov models
Pattern Recognition
The classification of noisy sequences generated by similar HMMs
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
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A novel feature generation scheme which combines multiclass mapping of Fisher scores and appearance based features for face recognition (FR) is proposed in this paper. Multi-class mapping of Fisher scores is based on partial derivative analysis of parameters of hidden Markov model (HMM), and appearance based features are obtained directed from face images. Linear discriminant analysis (LDA) is used to analyze the feature vectors generated under this scheme. Recognition performance improvement is observed over stand-alone HMM method as well as Fisherface method, which also uses appearance based feature vectors. Moreover, by reducing the number of models involved in the training and testing stages, the proposed feature generation scheme can maintain very high discriminative power at much lower computational complexity comparing to that of the traditional HMM based FR system. Experimental results are provided to demonstrate the viability of this scheme for face recognition.