A lot of research on face recognition has been conducted over the past two decades or more. Various face recognition methods have been proposed, but investigations are still underway to tackle different problems and challenges for face recognition. The existing algorithms can only solve some of the problems, and their performances degrade in real-world applications. In this talk, we will first discuss the performances of face recognition techniques on face images at different resolutions. Then, we discuss issues and methods for low-resolution and high-resolution face recognition. For low-resolution face recognition, we will present different approaches, and focus more on the use of feature super-resolution and fusion.
To perform face recognition, image features from a query image 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. To improve the performance, information about face images at the original low resolution and a higher resolution are considered. As the features from different resolutions should closely correlate with each other, we introduce the cascaded generalized canonical correlation analysis (GCCA) to fuse the information to form a single feature vector for face recognition. For recognition of HR face images, we will show that pore-scale facial features can be explored when the resolution of faces is greater than 700600 pixels. We will describe the use of the facial features for recognition under conditions of different facial expressions, lighting, poses and captured times. We will also present the minimum area in face images that can retain a high recognition level. Experiment results indicate that the facial pores can be used as a new biometric for recognition, even distinguishing between identical twins easily. Furthermore, the use of deep learning for face recognition will also be presented and discussed.
Kin-Man Lam received the Associateship in Electronic Engineering with distinction from The Hong Kong Polytechnic University (formerly called Hong Kong Polytechnic) in 1986, the M.Sc. degree in communication engineering from the Department of Electrical Engineering, Imperial College of Science, Technology and Medicine, London, U.K., in 1987, and the Ph.D. degree 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 same department 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 organizing committee and program committee of many international conferences. In particular, he was a General Co-Chair of IEEE ICSPCC 2012, APSIPA ASC 2015, and IEEE ICME2017, which were held in Hong Kong in August 2012, December 2015, and July 2017, respectively. Dr. Lam was also the Chairman of the IEEE Hong Kong Chapter of Signal Processing between 2006 and 2008. Between 2009 and 2013, he was an Associate Editor of IEEE Trans. on Image Processing.
Currently, Dr. Lam is VP-Member Relations and Development of the Asia-Pacific Signal and Information Processing Association (APSIPA), and the Director-Membership Services of the IEEE Signal Processing Society. He serves as an Associate Editor of Digital Signal Processing, APSIPA Trans. on Signal and Information Processing, and EURASIP International Journal on Image and Video Processing. He is also an Editor of HKIE Transactions, and an Area Editor of IEEE Signal Processing Magazine. His current research interests include human face recognition, image and video processing, and computer vision.