Gait Recognition Using Image Self-Similarity
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Gait Recognition Using Image Self-Similarity Chiraz BenAbdelkader Identix Corporation, One Exchange Place, Jersey City, NJ 07302, USA Email: [email protected]
Ross G. Cutler Microsoft Research, One Microsoft Way, Redmond, WA 98052-6399, USA Email: [email protected]
Larry S. Davis Department of Computer Science, University of Maryland, College Park, MD 20742, USA Email: [email protected] Received 30 October 2002; Revised 18 May 2003 Gait is one of the few biometrics that can be measured at a distance, and is hence useful for passive surveillance as well as biometric applications. Gait recognition research is still at its infancy, however, and we have yet to solve the fundamental issue of finding gait features which at once have sufficient discrimination power and can be extracted robustly and accurately from low-resolution video. This paper describes a novel gait recognition technique based on the image self-similarity of a walking person. We contend that the similarity plot encodes a projection of gait dynamics. It is also correspondence-free, robust to segmentation noise, and works well with low-resolution video. The method is tested on multiple data sets of varying sizes and degrees of difficulty. Performance is best for fronto-parallel viewpoints, whereby a recognition rate of 98% is achieved for a data set of 6 people, and 70% for a data set of 54 people. Keywords and phrases: gait recognition, human identification at a distance, human movement analysis, behavioral biometrics, pattern recognition.
1.
INTRODUCTION
1.1. Motivation Gait is a relatively new and emergent behavioral biometric [1, 2] that pertains to the use of an individual’s walking style (or “the way he walks”) to determine identity. Gait recognition is the term typically used in the computer vision community to refer to the automatic extraction of visual cues that characterize the motion of a walking person in video and is used for identification purposes. Gait is particularly an attractive modality for passive surveillance since, unlike most biometrics, it can be measured at a distance, hence not requiring interaction with or cooperation of the subject. However, gait features exhibit a high degree of intraperson variability, being dependent on various physiological, psychological, and external factors such as footwear, clothing, surface of walking, mood, illness, fatigue, and so forth. The question then arises as to whether there is sufficient gait variability between people that can discriminate them even in the presence of large variation within each individual. There is indeed strong evidence originating from psychophysical experiments [3, 4, 5] and gait analysis research
(a well-advanced multidisciplinary field that spans kinesiology, physiotherapy, orthopedic surgery, ergonomics, etc.) [6, 7, 8, 9, 10] that gait dynamics contain a signature that is characteristic of, and possibly unique to, each individual. From a biomechanics standpoint, human gait consists of synchronized, integrated movements of hundreds of muscles and joints of the bo
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