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A probabilistic image-weighting scheme for robust silhouette-based gait recognition

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Abstract

Many gait recognition methods use silhouettes as a feature due to their simplicity and effectiveness. However, silhouette-based gait recognition algorithms have the drawback of performance degradation when the silhouette images are corrupted. To solve this problem, this paper proposes a new gait representation method by emphasizing the noise-free silhouettes while suppressing the corrupted ones. The probabilistic support vector machine (PSVM) is employed to weigh the silhouette images according to quality and to construct a new gait representation for robust recognition. Experiments are conducted with the CASIA and SOTON databases, and the proposed method makes silhouette-based gait recognition as reliable biometrics.

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Notes

  1. We assign the classes of clear and noisy images to “+1” and “−1” respectively. Therefore the positive direction of the normal vector in the decision boundary points to the set of noise-free silhouettes.

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Yonsei University, Sinchon-dong, Seodaemun-gu, Seoul, 120-749, South Korea

    Heesung Lee, Jeonghyun Baek & Euntai Kim

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  1. Heesung Lee
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  2. Jeonghyun Baek
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  3. Euntai Kim
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Correspondence to Euntai Kim.

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Lee, H., Baek, J. & Kim, E. A probabilistic image-weighting scheme for robust silhouette-based gait recognition. Multimed Tools Appl 70, 1399–1419 (2014). https://doi.org/10.1007/s11042-012-1163-4

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