Abstract To solve the problems of the disaster of dimensionality and the shortage on describing the nonlinear model that the linear coupled metric learning has when solving practical problems,the kernel coupled metric learning is proposed by introducing kernel method. Firstly,the nonlinear transformations are used to map the data from different sets into a high dimensional coupled space to make the elements of two sets with correlation as close as possible to each other after the projection. Then,the traditional kernel method is used in the public coupled space. The proposed method is applied to gait recognition to solve the match problem of different sets. Experiments and analysis are made on the CASIA(B) gait database,and the experimental results show that the proposed method has satisfactory recognition results.
2013, Vol. 26 
