Hardware Architecture for Pseudo-Two-Dimensional Hidden-Markov-Model-Based Face Recognition Systems Employing Laplace Distribution Functions
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概要
- 論文の詳細を見る
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.
- Published by the Japan Society of Applied Physics through the Institute of Pure and Applied Physicsの論文
- 2007-04-30
著者
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Shibata Tadashi
Department Of Electrical Engineering And Information Systems School Of Engineering The University Of
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Suzuki Yasufumi
Department Of Frontier Informatics School Of Frontier Sciences The University Of Tokyo
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Suzuki Yasufumi
Department of Frontier Informatics, Graduate School of Frontier Sciences, The University of Tokyo, 701 Kiban Bldg., 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan
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Shibata Tadashi
Department of Frontier Informatics, Graduate School of Frontier Sciences, The University of Tokyo, 701 Kiban Bldg., 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561, Japan
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Shibata Tadashi
Department of Electrical Engineering and Information System, The University of Tokyo, Bunkyo, Tokyo 113-8656, Japan
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