Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Face Recognition: Features Versus Templates
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
A hidden markov model-based approach for face detection and recognition
A hidden markov model-based approach for face detection and recognition
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
Content based indexing of images and video using face detection and recognition methods
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Journal of Cognitive Neuroscience
Face recognition: a convolutional neural-network approach
IEEE Transactions on Neural Networks
Combination of Fisher scores and appearance based features for face recognition
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
Component-based discriminative classification for hidden Markov models
Pattern Recognition
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As the vital component of a recently developed stochastic modelbased feature generation scheme, Fisher score is increasingly usedin classification applications. In this work we present ageneralization of previous proposed feature generation schemes byintroducing the concept of multi-class mapping which is oriented tomulti-class classification problems. Based on the generalizedfeature generation scheme, a novel face recognition system isdeveloped by a systematical integration of hidden Markov model(HMM) and linear discriminant analysis (LDA).The proposed system isevaluated on a public available face database of 50 subjects.Comparing to holistic features based LDA method, stand alone HMMmethod, and LDA method basedon previous proposed feature generationschemes which are intrinsically oriented to two-class problems,superior performance is obtained by our method in terms ofrecognition accuracy.