Abstract
Human identification at a distance remains a challenging problem. Two biometric sources that are available in such situations are gait and face. In this paper, we present a new approach that utilizes and integrates information from frontal gait and face at the feature level. A novel kernel coupled mapping method is introduced to project both the gait features and the face features into a unified subspace where the heterogeneous modalities are transformed into the homologous features naturally. Moreover, the proposed feature level fusion scheme is compared with the match score level fusion schemes (Sum, Max and Product rules) and two feature level fusion schemes. The experimental results demonstrate that the proposed feature level fusion scheme outperforms the match score level and the other two feature level fusion schemes.
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Xing, X., Wang, K., Yang, X., Du, T. (2015). A Novel Feature Fusion Scheme for Human Recognition at a Distance. In: Yang, J., Yang, J., Sun, Z., Shan, S., Zheng, W., Feng, J. (eds) Biometric Recognition. CCBR 2015. Lecture Notes in Computer Science(), vol 9428. Springer, Cham. https://doi.org/10.1007/978-3-319-25417-3_64
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DOI: https://doi.org/10.1007/978-3-319-25417-3_64
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