Title :
Discriminant analysis of stochastic models and its application to face recognition
Author :
Chen, Ling ; Man, Hong
Author_Institution :
Dept. of Electr. & Comput. Eng., Stevens Inst. of Technol., Hoboken, NJ, USA
Abstract :
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.
Keywords :
covariance matrices; face recognition; feature extraction; hidden Markov models; image classification; maximum likelihood estimation; vectors; visual databases; covariance matrix; face database; face recognition; hidden Markov model; linear discriminant analysis; maximum likelihood estimation; multiclass image classification; stochastic model based feature vector generation; Application software; Face recognition; Hidden Markov models; Image databases; Indexing; Kernel; Linear discriminant analysis; Spatial databases; Stochastic processes; Support vector machines;
Conference_Titel :
Analysis and Modeling of Faces and Gestures, 2003. AMFG 2003. IEEE International Workshop on
Print_ISBN :
0-7695-2010-3
DOI :
10.1109/AMFG.2003.1240817