University of Nottingham Ningbo China
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Multi-resolution feature fusion for face recognition

Date(s)
15 November 2013 (10:30-11:30)
Description

There will be a presentation given by our guest Professor Kenneth K.M. Lam from the Centre for Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University.

About the presentation

For face recognition, image features are first extracted and then matched to those features in a gallery set. The amount of information and the effectiveness of the features used will determine the recognition performance. In this talk, a novel face recognition approach using information about face images at higher and lower resolutions will be presented, which can enhance the information content of the features that are extracted and combined at different resolutions. As the features from different resolutions should closely correlate with each other, we employ the cascaded generalised canonical correlation analysis (GCCA) to fuse the information to form a single feature vector for face recognition. To improve the performance and efficiency, we also employ "Gabor-Feature Hallucination", which predicts the high-resolution (HR) Gabor features from the Gabor features of a face image directly by using local linear regression. We also extend the algorithm to low-resolution (LR) face recognition, in which the medium-resolution (MR) and HR Gabor features of a LR input image are estimated directly. The LR Gabor features and the predicted MR and HR Gabor features are then fused using GCCA for LR face recognition. Our algorithm can avoid having to perform the interpolation/super-resolution of face images and having to extract HR Gabor features. Experimental results show that the proposed methods have a superior recognition rate and are more efficient than traditional methods.

Speaker biography

Kin-Man Lam received the Associateship in Electronic Engineering with distinction from The Hong Kong Polytechnic University (formerly Hong Kong Polytechnic) in 1986, MSc in communication engineering from the Department of Electrical Engineering, Imperial College of Science, Technology and Medicine, London, UK, in 1987, and a PhD from the Department of Electrical Engineering, University of Sydney, Sydney, Australia, in August 1996.

From 1990 to 1993, he was a Lecturer at the Department of Electronic Engineering, The Hong Kong Polytechnic University. He joined the Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, as an Assistant Professor in October 1996, became an Associate Professor in 1999, and has been a Professor since 2010. He has been a member of the organising committee and programme committee of many international conferences. In particular, he was the Secretary of the 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'03), the Technical Chair of the 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing (ISIMP 2004), a Technical Co-Chair of the 2005 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS 2005), a secretary of the 2010 International Conference on Image Processing (ICIP 2010), a Technical Co-Chair of 2010 Pacific-Rim Conference on Multimedia (PCM 2010), and a General Co-chair of the 2012 IEEE International Conference on Signal Processing, Communications, & Computing, which was held in Hong Kong in August 2012. Dr Lam was the Chairman of the IEEE Hong Kong Chapter of Signal Processing between 2006 and 2008.

Currently, Dr Lam is a BoG member of the Asia-Pacific Signal and Information Processing Association (APSIPA) and the Director-Student Services of the IEEE Signal Processing Society. Dr Lam also serves as an Associate Editor of IEEE Trans. on Image Processing, APSIPA Trans. on Signal and Information Processing, Digital Signal Processing, HKIE Transactions, and EURASIP International Journal on Image and Video Processing. His current research interests include human face recognition, image and video processing, and computer vision.