Person Identification by Walking Gesture Using Skeleton Sequences

When coping with person identification problem, previous approaches either directly take raw RGB as inputs or use more sophisticated devices to capture other information. However, most of the approaches are sensitive to the changes of environment and different clothing, little variation may lead to failure identification. Recent research shows that “gait” (i.e., a person’s manner of walking) is a unique trait of a human being. Motivated by this, we propose a novel method to identify people by their gaits. In order to figure out the characteristic of individual gait, we are interested in utilizing skeletal information, which is more robust to the diversification of environment and appearance. To effectively utilize skeletal data, we analyze the spatial relationship of joints and transform the 3D skeleton coordinates into relative distances and angles between joints, and then we use a bidirectional long short-term memory neural network to explore the temporal information of the skeleton sequences. Results show that our proposed method can outperform previous methods on BIWI and IAS-Lab datasets by gaining 10.33% accuracy improvement on average.