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Face Recognition Using Singular Value Decomposition along with seven state HMM


Anagha A. Shinde, Sachin D. Ruikar


Vol. 13  No. 12  pp. 117-122


This paper presents a new approach using Hidden Marko Model as classifier and Singular Values Decomposition (SVD) coefficients as features for face recognition. As face is a complex multi-dimensional structure and needs good computing techniques for recognition and it is an integral part of biometrics. Features extracted from a face are processed and compared with similar faces which exist in database. The recognition of human faces is carried out by comparing characteristics of the face to those of known individuals. Here seven state Hidden Markov Model (HMM)-based face recognition system is proposed.A small number of quantized Singular Value Decomposition (SVD) coefficients asfeatures describing blocks of face images. SVD is a method for transforming correlated variables into a set of uncorrelated ones that better expose the various relationships among the original data item. This makes the system very fast. The proposed method is compared with the best researches in the literature. The proposed approach has been examined on ORL database and some personal database The results show that the proposed method is the fastest one, having good accuracy.


Face Recognition, Singular Value Decomposition, Hidden Markov Model