Kernel coupled distance metric learning for gait recognition and face recognition

The performances of biometrics may be adversely impact by different walking states, walking directions, resolutions of gait sequence images, pose variation and low resolution of face images. To address these problems, we presented a kernel coupled distance metric learning (KCDML) method after considering matching among different data collections. By using a kernel trick and a specialized locality preserving criterion, we formulated the problem of kernel coupled distance metric learning as an optimization problem whose aims are to search for the pair-wise samples staying as close as possible and to preserve the local structure intrinsic data geometry. Instead of an iterative solution, one single generalized eigen-decomposition can be leveraged to compute the two transformation matrices for two classifications of data sets. The effectiveness of the proposed method is empirically demonstrated on gait and face recognition tasks' results which outperform four linear subspace solutions' (i.e. CDML, PCA, LPP, LDA) and four nonlinear subspace solutions' (i.e. Huang's method, PCA-RBF, KPCA, KLPP).