tailieunhanh - Báo cáo hóa học: " Research Article A Statistical Multiresolution Approach for Face Recognition Using Structural Hidden Markov Models"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article A Statistical Multiresolution Approach for Face Recognition Using Structural Hidden Markov Models | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 675787 13 pages doi 2008 675787 Research Article A Statistical Multiresolution Approach for Face Recognition Using Structural Hidden Markov Models P. Nicholl 1 A. Amira 2 D. Bouchaffra 3 and R. H. Perrott1 1 School of Electronics Electrical Engineering and Computer Science Queens University Belfast BT7 INN UK 2 Electrical and Computer Engineering School of Engineering and Design Brunel University London UB8 3PH UK 3 Department of Mathematics and Computer Science Grambling State University Carver Hall Room 281-C . Box 1191 la USA Correspondence should be addressed to P. Nicholl Received 30 April 2007 Revised 2 August 2007 Accepted 31 October 2007 Recommended by Juwei Lu This paper introduces a novel methodology that combines the multiresolution feature of the discrete wavelet transform DWT with the local interactions of the facial structures expressed through the structural hidden Markov model SHMM . A range of wavelet filters such as Haar biorthogonal 9 7 and Coiflet as well as Gabor have been implemented in order to search for the best performance. SHMMs perform a thorough probabilistic analysis of any sequential pattern by revealing both its inner and outer structures simultaneously. Unlike traditional HMMs the SHMMs do not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the SHMMs. Therefore the long-range dependency problem inherent to traditional HMMs has been drastically reduced. SHMMs have not previously been applied to the problem of face identification. The results reported in this application have shown that SHMM outperforms the traditional hidden Markov model with a 73 increase in accuracy. Copyright 2008 P. Nicholl et al. This is an open access article distributed under the Creative Commons Attribution License .