Local Kernel Feature Analysis (LKFA) for object recognition

Authors:
Baochang Zhang;Yongsheng Gao;Hong Zheng
Affiliations:
National Key Laboratory of Science and Technology on Integrated Control Technology, School of Automation Science and Electrical Engineering, Beihang University, Beijing, China;School of Engineering, Griffith University, Australia;National Key Laboratory of Science and Technology on Integrated Control Technology, School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
Venue:
Neurocomputing
Year:
2011

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Abstract

This paper proposes a new Local Kernel Feature Analysis (LKFA) method for object recognition. LKFA captures the nonlinear local relationship in an image via kernel functions. Different from traditional kernel methods for object recognition, the proposed method does not need to reserve the training samples. LKFA is designed to extract the eigenvalue features from the Hermite matrix of a local feature representation, which we have theoretically proven its robustness to noise and perturbations. Experiment results on palmprint and face recognitions demonstrated the effectiveness of the proposed LKFA that significantly improved the performance of the local feature based object recognition method.