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

Highlights：

• We propose a new loss function that guides the network to map the gait feature to a separable yet discriminative feature space. Our loss function utilizes A-Softmax to learn a separable feature in the cosine space and triplet loss to increase the distance between feature vectors of different subjects and decrease the distance between the feature vectors of the same subjects.

• A-Softmax and triplet loss are optimized in different spaces. In order to make the training process feasible, we add a batch-normalization layer after extracting gait feature (before the last fully-connected layer) to reduce the impact of optimizing two different losses.

• We conduct comprehensive experiments on CASIA-B dataset and TUM GAID dataset. The experiment results show that using our loss function with GaitSet as our backbone network exceeds the previous state-of-the-art performance under the same experiment settings.