Gait Recognition Based on Temporal Gait Information Enhancing

Gait recognition is a long range biometric technology that identifies individuals by their walking patterns. Currently, gait recognition primarily extracts gait features using convolutional neural networks, which are based on either the global appearance or local human body regions. However, the global feature methods are lack of long range interactions in different local regions and lose temporal features by some extent, and the local feature method segmenting gait silhouettes into blocks limits the ability to characterize local feature weights. In this paper, we propose a gait recognition method that enhances interactions between local regions. To implement this method, we construct a new feature enhancement module, which is a global and local feature extractor based on SENet (GLFES), to enhance the recognition of local features using the attention mechanism. Extensive experiments based on our proposed method have been conducted on the public datasets CASIA-B and OUMVLP to achieve state-of-the-art performances.