In this paper we propose a novel multimodal Bayesian approach based on PCA-LDA processing for person identification from low resolution surveillance video with cues extracted from gait and face biometrics. The experimental evaluation of the proposed scheme on a publicly available database [2] showed that the combined PCA-LDA face and gait features can lead to powerful identity verification and can capture the inherent multimodality in walking gait patterns and discriminate the identity from low resolution surveillance videos. © 2011 Springer-Verlag.
CITATION STYLE
Hossain, E., & Chetty, G. (2011). Multimodal identity verification based on learning face and gait cues. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7064 LNCS, pp. 1–8). https://doi.org/10.1007/978-3-642-24965-5_1
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