An Efficient Gait Recognition with Backpack Removal
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Research Article An Efficient Gait Recognition with Backpack Removal Heesung Lee, Sungjun Hong, and Euntai Kim Biometrics Engineering Research Center, School of Electrical and Electronic Engineering, Yonsei University, Sinchon-dong, Seodaemun-gu, Seoul 120-749, South Korea Correspondence should be addressed to Euntai Kim, [email protected] Received 12 February 2009; Accepted 12 August 2009 Recommended by Moon Kang Gait-based human identification is a paradigm to recognize individuals using visual cues that characterize their walking motion. An important requirement for successful gait recognition is robustness to variations including different lighting conditions, poses, and walking speed. Deformation of the gait silhouette caused by objects carried by subjects also has a significant effect on the performance of gait recognition systems; a backpack is the most common of these objects. This paper proposes methods for eliminating the effect of a carried backpack for efficient gait recognition. We apply simple, recursive principal component analysis (PCA) reconstructions and error compensation to remove the backpack from the gait representation and then conduct gait recognition. Experiments performed with the CASIA database illustrate the performance of the proposed algorithm. Copyright © 2009 Heesung Lee et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1. Introduction Gait recognition is the identification of individuals based on their walking style [1]. The theoretic foundation of gait recognition is the uniqueness of each person’s gait, as revealed by Murray et al. in 1964 [2]. Gait analysis has the advantage of being noninvasive and noncontact. Gait is also less likely to be obscured than other biometrics such as face, fingerprints, and iris. Furthermore, gait is the only biometric which can be perceived at a long distance [3]. Hence, the gait recognition system has recently attracted increasing interest from researchers in the field of computer vision. Gait recognition methods can be classified into two broad types: model-based and silhouette-based approaches [4]. Model-based approaches try to represent the human body or motion precisely by employing explicit models describing gait dynamics, such as stride dimensions and the kinematics of joint angles [5–7]. The effectiveness of model-based approaches, however, is still limited due to imperfect vision techniques in body structure/motion modeling and parameter recovery from a walking image sequence. Moreover, precise modeling makes model-based approaches computationally expensive.
By contrast, the silhouette-based approaches characterize body movement using statistics of the walking patterns which capture both static and dynamic properties of body shape [8–15]. In these approaches, the representation methods for human gait obviously play a critical part. Several methods of this type have been repo
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