Abstract
The aim of person re-identification (ReID) is to recognize the same persons across different scenes. Due to the many demanding applications that utilize large-scale data, more and more attention has been devoted to matching efficiency and accuracy. Many methods that are based on binary coding have been presented to reach efficient ReID. Those methods learn projections to map the high-dimensional features into deep neural networks or compact binary codes through simple insertion of an extra fully connected layer with tanh-like activation. Nevertheless, the former approach needs hand-crafted feature extraction that also wastes a lot of time and complex (discrete) optimizations. In contrast, the latter approach lacks the essential discriminative information to a large extent because of the straightforward activation functions. A ReID framework is proposed in the current work, and it is inspired by the adversarial framework depending on the new regularization approach (ABC-NReg). We embedded the discriminative network into adversarial binary coding (ABC) with our new regularization, which improved the discriminative power combined with the triplet network. ABC-NReg and triplet networks were optimized, and three large-scale benchmark datasets, namely CUHK03, Market-1501, and DukeMTMC-reID datasets, were utilized to test the performance of our proposed model. We further compared the simulation results with the present hashing and non-hashing algorithms. Our model provided better results than other present models using the Market-1501 and DukeMTMC-reID datasets when considering Rank-1. For CUHK03 dataset, the proposed model exceeded the performance of other works when considering Rank 5 and Rank 20.