Face recognition in global harmonic subspace

Authors:
Richard M. Jiang;Danny Crookes;Nie Luo
Affiliations:
Computer Science Department, Loughborough University, Loughborough, UK;ECIT, School of Electromcs, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK;School of Engineering, University of Illinois, Urbana Champagne, IL
Venue:
IEEE Transactions on Information Forensics and Security
Year:
2010

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Abstract

In this paper, a novel pattern recognition scheme, global harmonic subspace analysis (GHSA), is developed for face recognition. In the proposed scheme, global harmonic features are extracted at the semantic scale to capture the 2-D semantic spatial structures of a face image. Laplacian Eigenmap is applied to discriminate faces in their global harmonic subspace. Experimental results on the Yale and PIE face databases show that the proposed GHSA scheme achieves an improvement in face recognition accuracy when compared with conventional subspace approaches, and a further investigation shows that the proposed GHSA scheme has impressive robustness to noise.