Weighted bilinear coding over salient body parts for person re-identification

Deep convolutional neural networks (CNNs) have demonstrated dominant performance in person re -identification (Re-ID). Existing CNN based methods utilize global average pooling (GAP) to aggregate intermediate convolutional features for Re-ID. However, this strategy only considers the first-order statis-tics of local features and treats local features at different locations equally important, leading to sub-optimal feature representation. To deal with these issues, we propose a novel weighted bilinear coding (WBC) framework for local feature aggregation in CNN networks to pursue more representative and dis-criminative feature representations, which can adapt to other advanced methods and improve their per-formance. In specific, bilinear coding is used to encode the channel-wise feature correlations to capture richer feature interactions. Meanwhile, a weighting scheme is applied on the bilinear coding to adaptively adjust the weights of local features at different locations based on their importance in recognition, further improving the discriminability of feature aggregation. To handle the spatial misalignment issue, we use a salient part net to derive salient body parts, and apply the WBC model on each part. The final represen-tation, formed by concatenating the WBC encoded features of each part, is both discriminative and resis-tant to spatial misalignment. Experiments on three benchmarks including Market-1501, DukeMTMC-reID and CUHK03 evidence the favorable performance of our method against other outstanding methods. (C) 2020 Elsevier B.V. All rights reserved.