Face Recognition in Global Harmonic Subspace

Richard Jiang, Danny Crookes, Nie Luo

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)416-424
JournalIEEE Transactions on Information Forensics and Security
Issue number3
Publication statusPublished - 2010


Dive into the research topics of 'Face Recognition in Global Harmonic Subspace'. Together they form a unique fingerprint.

Cite this