Gait recognition using Sparse Grassmannian Locality Preserving Discriminant Analysis

One of the greatest challenges for gait recognition is identification across appearance change. In this paper, we present a gait recognition method called Sparse Grassmannian Locality Preserving Discriminant Analysis. The proposed method learns a compact and rich representation of the gait images through sparse representation. The use of Grassmannian locality preserving discriminant analysis further optimizes the performance by preserving both global discriminant and local geometrical structure of the gait data. Experiments demonstrate that the proposed method can tolerate variation in appearance for gait identification effectively.