Advancing Person Re-Identification: Tensor-based Feature Fusion and Multilinear Subspace Learning

• URL: http://arxiv.org/abs/2312.16226v1
• Date: Sun, 24 Dec 2023 16:19:22 GMT
• Title: Advancing Person Re-Identification: Tensor-based Feature Fusion and Multilinear Subspace Learning
• Authors: Akram Abderraouf Gharbi, Ammar Chouchane, Abdelmalik Ouamane
• Abstract summary: We propose a novel PRe-ID system that combines feature representation and multilinear subspace learning. Our method exploits the power of pre-trained Conrimial Neural Networks (CNNs) as a strong deep feature extractor. We evaluate our approach by conducting experiments on three datasets VIPeR, GRID, and PRID450s.
• Score: 0.6829272097221595
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Person re-identification (PRe-ID) is a computer vision issue, that has been a fertile research area in the last few years. It aims to identify persons across different non-overlapping camera views. In this paper, We propose a novel PRe-ID system that combines tensor feature representation and multilinear subspace learning. Our method exploits the power of pre-trained Convolutional Neural Networks (CNNs) as a strong deep feature extractor, along with two complementary descriptors, Local Maximal Occurrence (LOMO) and Gaussian Of Gaussian (GOG). Then, Tensor-based Cross-View Quadratic Discriminant Analysis (TXQDA) is used to learn a discriminative subspace that enhances the separability between different individuals. Mahalanobis distance is used to match and similarity computation between query and gallery samples. Finally, we evaluate our approach by conducting experiments on three datasets VIPeR, GRID, and PRID450s.

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