hmm-based face recognition using svd and half of the face image

Speeding up the system is one of the basic challenges in the real-world applications of face recognition (fr), whereas reducing the computational complexity can significantly increase the speed of the system. in recent years, many face recognition methods have been proposed but few of them give attention to this issue. accordingly, in this article, we take the axis-symmetrical property of faces as a novel idea to speed up the face recognition algorithm as well as to reduce the computational complexity. taking the axis-symmetrical property of faces leads us to use half of the face image. proposing a face recognition system using hidden markov model (hmm) as a classifier, we use the singular value decomposition (svd) to build the observation vectors. evaluated results of the proposed system on yale and faces94 datasets show that the proposed system can achieve a satisfactory recognition rate with a higher speed.