Local feature hashing for face recognition

Authors:
Zhihong Zeng;Tianhong Fang;Shishir Shah;Ioannis A. Kakadiaris
Affiliations:
Departments of Computer Science, Electrical and Computer Engineering and Biomedical Engineering, University of Houston;Departments of Computer Science, Electrical and Computer Engineering and Biomedical Engineering, University of Houston;Departments of Computer Science, Electrical and Computer Engineering and Biomedical Engineering, University of Houston;Departments of Computer Science, Electrical and Computer Engineering and Biomedical Engineering, University of Houston
Venue:
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Year:
2009

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Abstract

In this paper, we present Local Feature Hashing (LFH), a novel approach for face recognition. Focusing on the scalability of face recognition systems, we build our LFH algorithm on the p-stable distribution Locality-Sensitive Hashing (pLSH) scheme that projects a set of local features representing a query image to an ID histogram where the maximum bin is regarded as the recognized ID. Our extensive experiments on two publicly available databases demonstrate the advantages of our LFH method, including: i) significant computational improvement over naive search; ii) hashing in high-dimensional Euclidean space without embedding; and iii) robustness to pose, facial expression, illumination and partial occlusion.