Abstract
The information contained in gait frames is different, and the contribution of different frames to recognition tasks is also different. However, each frame has the same degree of attention in the input layer, this prevents the network from focusing on keyframes. Therefore, we propose a keyframe extraction module via information weighting, make network can pay more attention to the high contribution frame at the input layer, and the extraction of the distinctive features is improved. Moreover, the range of motion in different parts of the human body is different, the temporal and spatial correlation of local feature between silhouettes is different. Based on the discovery, we propose a Local Features Flow Regulation module to calculate the correlation coefficient of the local features of each silhouette, and the regulation coefficient is generated by the correlation coefficient. The regulation coefficient is applied to regulate the flow of local features, this enables the network to capture areas with more spatial and temporal features. Through the extraction of frame-level features and the interaction of local features between frames, the network can extract the most discriminative features from global to local flexibly. During training, each horizontal part is trained separately. The training can adjust the regulation coefficients, and the network is more flexible and expressive. Our model has a good performance on cross-viewing and complex environments of CASIA-B dataset. In the case of normal environment and complex environment (pedestrian with backpacks and in coats), the rank-1 of the proposed model is 95.1%, 87.9% and 74.0% respectively, higher than state-of-the-art.
Highlights
Gait is a non-cooperative biometric feature, and the gait can be used to identify the identity of a pedestrian within a few tens of meters
In this paper, based on the above motivation and current research status of gait recognition, we propose a flexible and simple network structure. with three contributions: 1) In order to increase the contribution of key frames to feature extraction, the gait silhouette sequence in the network is readjusted by using the attention mechanism based on the information it contains
We propose attention-based key frame extraction based on information weighting in gait sequences, that and efficiently extract the key frames with this attention mechanism
Summary
Gait is a non-cooperative biometric feature, and the gait can be used to identify the identity of a pedestrian within a few tens of meters. The Gait Energy Image (GEIs) [16] and the gait silhouette sequences are used as input by many deep learning models to extract features directly from a backbone network. This input makes each frame important to the network, without considering the importance of each frame, and does not pay attention to the spatiotemporal correlation locally on the gait silhouette sequences. Current gait recognition methods based on deep learning mainly extract global features from each image in the sequence. These network structures have been unable to capture the correlation and spatiotemporal information of local features between frames.
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