Hardware architecture for pseudo-two-dimensional hidden-markov-model-based face recognition systems employing laplace distribution functions

被引：0
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作者：
Suzuki, Yasufumi [1 ]
Shibata, Tadashi [1 ]
机构：
[1] Department of Frontier Informatics, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan
来源：
Japanese Journal of Applied Physics, Part 1: Regular Papers and Short Notes and Review Papers | 2007年 / 46卷 / 4 B期
关键词：
A hardware architecture for pseudo-two-dimensional (2D) hidden-Markov-model-based face recognition systems has been developed. The proposed architecture employs the state-parallel organization in which each processing element represents each state in the pseudo-2D hidden Markov model. To reduce the area of processing elements; the mixture of Laplace distributions has been utilized for an observation probability function instead of the mixture of Gaussian distributions. To verify the concept; the proposed architecture has been implemented in a field programmable gate array (FPGA). As a result; the number of logic gates has been reduced by 47% as compared with that using Gaussian distibutions and more than 97% recognition rate has been achieved for the AT&T face database. The processor takes only 44.2 ms for identifying a facial image from 40 people at 100 MHz clock frequency; thus enabling us to build real-time responding systems. © 2007 The Japan Society of Applied Physics;
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Journal article (JA)
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页码：2265 / 2270
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