Abstract
Gait recognition stands as a crucial method for distant person identification. Most state-of-the-art gait recognition frameworks consists of two modules: feature extraction and feature matching. The fixed nature of each part in these modules leads to suboptimal performance in challenging conditions as they are mutually independent. This paper presents the integration of those steps into a single framework. Specifically, we design a unified end-to-end convolutional neural network (CNN) for learning the efficient gait representation and gait recognition. Since dynamic areas contain the most informative part of the human gait and are insensitive to changes in various covariate conditions, we feed the gait entropy images as input to CNN model to capture mostly the motion information. The proposed method is evaluated through experiments conducted on the CASIA-B dataset, specifically for cross-view and cross-walking gait recognition. The experimental results strongly indicate the effectiveness of the approach.
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Nahar, S., Narsingani, S., Patel, Y. (2023). A Unified Convolutional Neural Network for Gait Recognition. In: Lu, H., Blumenstein, M., Cho, SB., Liu, CL., Yagi, Y., Kamiya, T. (eds) Pattern Recognition. ACPR 2023. Lecture Notes in Computer Science, vol 14407. Springer, Cham. https://doi.org/10.1007/978-3-031-47637-2_18
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DOI: https://doi.org/10.1007/978-3-031-47637-2_18
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