An improved biometrics technique based on metric learning approach

Authors:
Xianye Ben;Weixiao Meng;Rui Yan;Kejun Wang
Affiliations:
School of Information Science and Engineering, Shandong University, No. 27, Shanda South Road, Jinan 250100, China;School of Electronics Information Engineering, Harbin Institute of Technology, Harbin 150080, China;Computer Science Department, Rensselaer Polytechnic Institute, Troy, NY 12180, USA;College of Automation, Harbin Engineering University, Harbin 150001, China
Venue:
Neurocomputing
Year:
2012

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Abstract

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.