Discriminant analysis of stochastic models and its application to face recognition

• Published in: IEEE International Workshop on Analysis and Modeling of Faces and Gestures : AMFG 2003 : 17 October, 2003, Nice, France pp. 5 - 10
• Main Authors: Chen, L., Man, H.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2003
• Subjects: Application software; Face recognition; Hidden Markov models; Image databases; Indexing; Kernel; Linear discriminant analysis; Spatial databases; Stochastic processes; Support vector machines
• ISBN: 0769520103; 9780769520100
• DOI: 10.1109/AMFG.2003.1240817

As the vital component of a recently developed stochastic model based feature generation scheme, Fisher score is increasingly used in classification applications. We present a generalization of previous proposed feature generation schemes by introducing the concept of multiclass mapping, which is oriented to multiclass classification problems. Based on the generalized feature generation scheme, a novel face recognition system is developed by a systematical integration of hidden Markov model (HMM) and linear discriminant analysis (LDA). The proposed system is evaluated on a public available face database of 50 subjects. Comparing to holistic features based LDA method, stand alone HMM method, and LDA method based on previous proposed feature generation schemes, which are intrinsically oriented to two-class problems, superior performance is obtained by our method in terms of recognition accuracy.