An improved biometrics technique based on metric learning approach

A biometrics technique based on metric learning approach is proposed in this paper to achieve higher correct classification rates under the condition that the feature of the query is very different from that of the register for a given individual. Inspired by the definition of generalized distance, the criterion of this new metric learning is defined by finding an embedding that preserves local information and obtaining a subspace that best detects the essential manifold structure. Furthermore, the two transformation matrices for the query and the register are obtained by a generalized eigen-decomposition. Experiments tested on biometric applications of CASIA(B) gait database and the UMIST face database, demonstrate that our proposed method performs better than classical metric learning methods and the current radial basis function (RBF) algorithms.