Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2011, Article ID 790598, 14 pages doi: Research Article Face Recognition from Still Images to Video Sequences: A Local-Feature-Based Framework Shaokang Chen,1, 2 Sandra Mau,1, 2 Mehrtash T. Harandi,1, 2 Conrad Sanderson,1, 2 Abbas Bigdeli,1, 2 and Brian C. Lovell1, 2 1 2 School NICTA, St Lucia, QLD 4072, Australia of ITEE, The University of Queensland, St Lucia, QLD 4072, Australia Correspondence should be addressed to Shaokang Chen, Received 30 April 2010; Revised 30 August 2010; Accepted 9 December 2010 Academic Editor: Luigi Di Stefano Copyright © 2011 Shaokang Chen et al. This. | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2011 Article ID 790598 14 pages doi 2011 790598 Research Article Face Recognition from Still Images to Video Sequences A Local-Feature-Based Framework Shaokang Chen 1 2 Sandra Mau 1 2 Mehrtash T. Harandi 1 2 Conrad Sanderson 1 2 Abbas Bigdeli 1 2 and Brian C. Lovell1 2 1NICTA St Lucia QLD 4072 Australia 2School ofITEE The University of Queensland St Lucia QLD 4072 Australia Correspondence should be addressed to Shaokang Chen Received 30 April 2010 Revised 30 August 2010 Accepted 9 December 2010 Academic Editor Luigi Di Stefano Copyright 2011 Shaokang Chen et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Although automatic faces recognition has shown success for high-quality images under controlled conditions for video-based recognition it is hard to attain similar levels of performance. We describe in this paper recent advances in a project being undertaken to trial and develop advanced surveillance systems for public safety. In this paper we propose a local facial feature based framework for both still image and video-based face recognition. The evaluation is performed on a still image dataset LFW and a video sequence dataset MOBIO to compare 4 methods for operation on feature feature averaging Avg-Feature Mutual Subspace Method MSM Manifold to Manifold Distance MMS and Affine Hull Method AHM and 4 methods for operation on distance on 3 different features. The experimental results show that Multi-region Histogram MRH feature is more discriminative for face recognition compared to Local Binary Patterns LBP and raw pixel intensity. Under the limitation on a small number of images available per person feature averaging is more reliable than MSM MMD and AHM and is much faster. Thus our
