Abstract
The recognition of a person from his or her gait has been a recent focus in computer vision because of its unique advantages such as being non-invasive and human friendly. However, gait recognition is not as reliable an identifier as other biometrics. In this paper, we applied a hierarchical fair competition-based parallel genetic algorithm and a neural network ensemble to the gait recognition problem. A diverse set of potential neural networks are generated to increase the reliability of the gait recognition, not only the best ones. Furthermore, a set of component neural networks is selected to build a gait recognition system such that generalization errors are minimized and negative correlation is maximized. Experiments are carried out with the NLPR and SOTON gait databases and the effectiveness of the proposed method for gait recognition is demonstrated and compared to previous methods.
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Recommended by Associate Editor Sungshin Kim under the direction of Editor Young-Hoon Joo.
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2013R1A2A2A01015624).
Heesung Lee received his B.S., M.S., and Ph.D. degrees in Electrical and Electronic Engineering from Yonsei University, Seoul, Korea, in 2003, 2005, and 2010, respectively. He is currently a Managing Researcher of Image Sensing Lab. in Samsung S1 Co., Ltd. His current research interests include computational intelligence, visual surveillance, pattern recognition, biometrics, and neural networks.
Heejin Lee received his B.S., M.S., and Ph.D. degrees in Electronic Engineering, from Yonsei University, Seoul, Korea, in 1987, 1989, and 1998, respectively. He was a Researcher in Daewoo Telecom Ltd., Seoul, Korea, from 1989 to 1993. He is currently a Professor in the Dept. of Electrical, Electronic and Control Engineering, Hankyong National University, Gyeonggi-do, Korea. His current research interests include fuzzy control theory, fuzzy application system, adaptive and robust control, robotics and automation.
Euntai Kim received his B.S., M.S., and Ph.D. degrees in Electronic Engineering from Yonsei University, Seoul, Korea, in 1992, 1994, and 1999, respectively. He was a full-time lecturer with the Department of Control and Instrumentation Engineering, Hankyong National University, Gyeonggi-do, Korea from 1999 to 2002. Since 2002, he has been with the faculty of the School of Electrical and Electronic Engineering, Yonsei University, where he is currently a Professor. He was a visiting researcher at Berkeley Initiative in Soft Computing, UC at Berkeley, Berkeley, CA, USA. His current research interests include computational intelligence and statistical machine learning and their application to intelligent robot, vehicle, and machine vision.
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Lee, H., Lee, H. & Kim, E. A new gait recognition system based on hierarchical fair competition-based parallel genetic algorithm and selective neural network ensemble. Int. J. Control Autom. Syst. 12, 202–207 (2014). https://doi.org/10.1007/s12555-012-0154-6
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DOI: https://doi.org/10.1007/s12555-012-0154-6