GaitAL-EI: a dual-branch CNN integrating gait energy images and video sequences for person identification
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Từ khóa:
Gait Recognition
Convolution Autoencoder
Gait Energy Image
Tóm tắt
Biometric-based identity recognition from images has attracted substantial research interest in recent years, driven by the growing need for reliable and non-intrusive methods of human identification. Among various approaches, gait recognition has emerged as a promising method due to its key advantages, including the ability to identify individuals at a distance and under diverse conditions. In this paper, we propose a deep convolutional neural network, called GaitAL-EI, to learn gait motion features through a dual-branch architecture that processes both gait energy images and video sequences. Our proposed method is trained and evaluated on public datasets. Comparative experiments demonstrate that the proposed approach achieves superior performance compared to the state-of-the-art GaitSet method, highlighting the effectiveness of integrating both static and dynamic gait information.
Tài liệu tham khảo
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Oct 31, 2025
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Tran-Chi, C., H. T. M. Tran, và T. Ho-Phuoc. “GaitAL-EI: A Dual-Branch CNN Integrating Gait Energy Images and Video Sequences for Person Identification”. Tạp Chí Khoa học Và Công nghệ Đại học Đà Nẵng, vol 23, số p.h 10B, Tháng Mười 2025, tr 70-75, doi:10.31130/ud-jst.2025.23(10B).640E.
