Robust face recognition using posterior union model based neural networks

J. Lin; J. Ming; D. Crookes

Robust face recognition using posterior union model based neural networks

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Robust face recognition using posterior union model based neural networks

Author(s): J. Lin ; J. Ming ; D. Crookes
DOI: 10.1049/iet-cvi.2008.0043

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Author(s): J. Lin ^{1, 2} ; J. Ming ² ; D. Crookes ²
- Affiliations: 1: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, People's Republic of China
  2: School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK
Source: Volume 3, Issue 3, September 2009, p. 130 – 142
DOI: 10.1049/iet-cvi.2008.0043 , Print ISSN 1751-9632, Online ISSN 1751-9640

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Face recognition with unknown, partial distortion and occlusion is a practical problem, and has a wide range of applications, including security and multimedia information retrieval. The authors present a new approach to face recognition subject to unknown, partial distortion and occlusion. The new approach is based on a probabilistic decision-based neural network, enhanced by a statistical method called the posterior union model (PUM). PUM is an approach for ignoring severely mismatched local features and focusing the recognition mainly on the reliable local features. It thereby improves the robustness while assuming no prior information about the corruption. We call the new approach the posterior union decision-based neural network (PUDBNN). The new PUDBNN model has been evaluated on three face image databases (XM2VTS, AT&T and AR) using testing images subjected to various types of simulated and realistic partial distortion and occlusion. The new system has been compared to other approaches and has demonstrated improved performance.

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