Pose-based Deep Gait Recognition
This work addresses person identification when other biometrics are unavailable, but it is incremental as it builds on existing pose-based and optical flow techniques.
The paper tackles gait recognition by using pose-based motion information from joint areas instead of full silhouettes, and reports that the proposed deep convolutional model outperforms state-of-the-art methods.
Human gait or walking manner is a biometric feature that allows identification of a person when other biometric features such as the face or iris are not visible. In this paper, we present a new pose-based convolutional neural network model for gait recognition. Unlike many methods that consider the full-height silhouette of a moving person, we consider the motion of points in the areas around human joints. To extract motion information, we estimate the optical flow between consecutive frames. We propose a deep convolutional model that computes pose-based gait descriptors. We compare different network architectures and aggregation methods and experimentally assess various sets of body parts to determine which are the most important for gait recognition. In addition, we investigate the generalization ability of the developed algorithms by transferring them between datasets. The results of these experiments show that our approach outperforms state-of-the-art methods.