Recursive spatiotemporal subspace learning for gait recognition

In this paper, we propose a new gait recognition method using recursive spatiotemporal subspace learning. In the first stage, periodic dynamic feature of gait over time is extracted by Principal Component Analysis (PCA) and gait sequences are represented in the form of Periodicity Feature Vector (PFV). In the second stage, shape feature of gait over space is extracted by Discriminative Locality Alignment (DLA) based on the PFV representation of gait sequences. After the recursive subspace learning, gait sequence data is compressed into a very compact vector named Gait Feature Vector (GFV) which is used for individual recognition. Compared to other gait recognition methods, GFV is an effective representation of gait because the recursive spatiotemporal subspace learning technique extracts both the shape features and the dynamic features. And at the same time, representing gait sequences in PFV form is an efficient way to save storage space and computational time. Experimental result shows that the proposed method achieves highly competitive performance with respect to the published gait recognition approaches on the USF HumanID gait database.