A sparse and discriminative tensor to vector projection for human gait feature representation

In this paper, we introduce an efficient tensor to vector projection algorithm for human gait feature representation and recognition. The proposed approach is based on the multi-dimensional or tensor signal processing technology, which finds a low-dimensional tensor subspace of original input gait sequence tensors while most of the data variation has been well captured. In order to further enhance the class separability and avoid the potential overfitting, we adopt a discriminative locality preserving projection with sparse regularization to transform the refined tensor data to the final vector feature representation for subsequent recognition. Numerous experiments are carried out to evaluate the effectiveness of the proposed sparse and discriminative tensor to vector projection algorithm, and the proposed method achieves good performance for human gait recognition using the sequences from the University of South Florida (USF) HumanID Database.