Abstract
In this paper, we propose a novel feature extraction method for the identification of humans. The main objective of our method is to identify each human being by extracting the Gabor feature based on the Adaptive Motion Model (AMM) for the motion of humans. In our method, the adaptive motion model, which can represent the temporal motion for each walking human is first made from the sequence images and, then, the Gabor features of the eight directions which can represent the spatial motion information for humans are extracted. The proposed feature extraction method can make a more accurate motion model by adjusting the weight between the previous and current model for each person. Moreover, our method has the advantage of allowing more information such as the Gabor features for the eight directions extracted from the AMM. Since the conventional method uses the face feature for each human being, it has disadvantages in the case of images of small size, while our method has better identification performance this case, because it only uses the spatio-temporal motion information. Finally, we identify each person by finding the minimum value of the extended dynamic time warping (DTW) for the eight Gabor features. The accuracy of the identification conducted using the proposed feature is better than that of the conventional method using the Gait Energy Image (GEI) and Face Image feature.
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Recommended by Editorial Board member Jang Myung Lee under the direction of Editor Young-Hoon Joo.
Junbum Park received the B.S. degree in Information Communication Engineering from Hansung University, Seoul, Korea, in 2001 and the M.S. degree in Electronics Engineering from Korea University, Seoul, Korea, in 2003. He is currently a Ph.D. student in Electronics Engineering at Korea University, Seoul, Korea. His current research interests include moving object detection, tracking and recognition in robot vision.
Younghyun Lee received the B.S. degree in Electrical Engineering from Korea University, Seoul, Korea, in 2007. He is currently a master’s course in Electrical and Electronics Engineering at Korea University, Seoul, Korea. His current research interests include moving object detection and target tracking in computer vision.
Hanseok Ko received the B.S. degree from Carnegie Mellon University, in 1982, the M.S. degree from the Johns Hopkins University, in 1988, and Ph.D. degree from the Catholic University of America, in 1992, all in Electrical Engineering. At the onset of his career, he was with the WOL, Maryland, where his work involved signal and image processing for the detection of and tracking of moving objects. Later, he developed and implemented data fusion algorithms in the same Laboratory. In addition, he was an Adjunct faculty member in the Department of Electrical Engineering at UMBC from 1992 to 1995. In March of 1995, he joined the faculty of the Department of Electronics and Computer Engineering at Korea University, where he is currently a Professor. In 2001, he was a Visiting Professor in the ECE Dept, Johns Hopkins University. His professional interests include speech/image signal processing for pattern recognition, multi-modal analysis, and intelligent data fusion.
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Park, J., Lee, Y. & Ko, H. Dynamic time warping based identification using gabor feature of Adaptive Motion Model for walking humans. Int. J. Control Autom. Syst. 7, 817–823 (2009). https://doi.org/10.1007/s12555-009-0514-z
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DOI: https://doi.org/10.1007/s12555-009-0514-z