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People Identification and Tracking Through Fusion of Facial and Gait Features

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Biometric Authentication (BIOMET 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8897))

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Abstract

This paper reviews the contemporary (face, gait, and fusion) computational approaches for automatic human identification at a distance. For remote identification, there may exist large intra-class variations that can affect the performance of face/gait systems substantially. First, we review the face recognition algorithms in light of factors, such as illumination, resolution, blur, occlusion, and pose. Then we introduce several popular gait feature templates, and the algorithms against factors such as shoe, carrying condition, camera view, walking surface, elapsed time, and clothing. The motivation of fusing face and gait, is that, gait is less sensitive to the factors that may affect face (e.g., low resolution, illumination, facial occlusion, etc.), while face is robust to the factors that may affect gait (walking surface, clothing, etc.). We review several most recent face and gait fusion methods with different strategies, and the significant performance gains suggest these two modality are complementary for human identification at a distance.

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Authors and Affiliations

  1. Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK

    Yu Guan, Xingjie Wei & Chang-Tsun Li

  2. Faculty of Engineering, Bar Ilan University, Ramat Gan, 52900, Israel

    Yosi Keller

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  1. Yu Guan
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  2. Xingjie Wei
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  3. Chang-Tsun Li
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  4. Yosi Keller
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Correspondence to Yu Guan .

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Editors and Affiliations

  1. University of Pavia, Pavia, Italy

    Virginio Cantoni

  2. Bulgarian Academy of Sciences, Sofia, Bulgaria

    Dimo Dimov

  3. University of Sassari, Alghero, Sassari, Italy

    Massimo Tistarelli

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Guan, Y., Wei, X., Li, CT., Keller, Y. (2014). People Identification and Tracking Through Fusion of Facial and Gait Features. In: Cantoni, V., Dimov, D., Tistarelli, M. (eds) Biometric Authentication. BIOMET 2014. Lecture Notes in Computer Science(), vol 8897. Springer, Cham. https://doi.org/10.1007/978-3-319-13386-7_17

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  • DOI: https://doi.org/10.1007/978-3-319-13386-7_17

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