In order to solve the problem of the curse of dimensionality and the small sample problem

a kernel sparsity preserving projection is proposed. First

the nonlinear transformation is used to map the original data to a high-dimensional feature space. Then

the sparsity reconstruction in a high-dimensional space is used and

the coefficient matrix is reduced and optimized. Finally

the projection matrix is obtained. This method is evaluated on the CASIA (B) Gait database. The experimental results show that the proposed method can obtain stable classification and performs satisfactory recognition results.