A unified perspective of classification-based loss and distance-based loss for cross-view gait recognition

Gait can be used to recognize people in an uncooperative and noninvasive manner and it is hard to imi-tate or counterfeit, which makes it suitable for video surveillance. The current solutions for gait recogni-tion are still not robust to handle the conditions when the view angles of the gallery and query are differ-ent. We improve the performance of cross-view gait recognition from the perspective of metric learning. Specifically, we propose to use angular softmax loss to impose an angular margin for extracting separa-ble features. At the same time, we use triplet loss to make the extracted features more discriminative. Additionally, we add a batch-normalization layer after extracting gait features to effectively optimize two different losses. We evaluate our approach on two widely-used gait dataset: CASIA-B dataset and TUM GAID dataset. The experiment results show that our approach outperforms the prior state-of-the-art ap-proaches, which shows the effectiveness of our approach. (c) 2021 Elsevier Ltd. All rights reserved.