Face recognition from still images to video sequences: A local-feature-based framework

Chen, Shaokang, Mau, Sandra, Harandi, Mehrtash T., Sanderson, Conrad, Bigdeli, Abbas and Lovell, Brian C. (2011) Face recognition from still images to video sequences: A local-feature-based framework. EURASIP Journal on Image and Video Processing, 2011 790598.1-790598.14. doi:10.1155/2011/790598

Author Chen, Shaokang
Mau, Sandra
Harandi, Mehrtash T.
Sanderson, Conrad
Bigdeli, Abbas
Lovell, Brian C.
Title Face recognition from still images to video sequences: A local-feature-based framework
Journal name EURASIP Journal on Image and Video Processing   Check publisher's open access policy
ISSN 1687-5281
Publication date 2011-02-01
Sub-type Article (original research)
DOI 10.1155/2011/790598
Open Access Status DOI
Volume 2011
Start page 790598.1
End page 790598.14
Total pages 14
Place of publication Heidelberg, Germany
Publisher SpringerOpen
Collection year 2012
Language eng
Abstract 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 proposed framework—averaging MRH feature is more suitable for CCTV surveillance systems with constraints on the number of images and the speed of processing.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Article ID 790598

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2012 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 4 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 11 times in Scopus Article | Citations
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Created: Fri, 11 Feb 2011, 10:29:50 EST by Dr Shaokang Chen on behalf of School of Information Technol and Elec Engineering