Affiliations: [a]
School of Information and Technology, Guangzhou Panyu Polytechnic, Guangzhou, P.R. China
| [b] School of Economics and Commerce, South China University of Technology, Guangzhou, P.R. China
| [c] Department of Interventional Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, P.R. China
| [d] School of Computer Science and Engineering, South China University of Technology, Guangzhou, P.R. China
Correspondence:
[*]
Corresponding author. Xiao-Yu Huang, School of Economics and Commerce, South China University of Technology, Guangzhou 510006, P.R. China. E-mail: [email protected].
Abstract: This paper addresses the problem of Face Recognition based on Image Set (FRIS) by kernel learning and proposed an extended kernel discriminant analysis framework for FRIS. By support vector machine learning, an image set from the original input space is mapped into the model space and described with Support Vector Domain Description (SVDD) to handle the underlying non-linearity in data space. In model space, a hyper-sphere encloses most of the mapped data, and the outliers lie outside the hyper-sphere. By exploring an efficient metric for the data domains in model space, we derive a kernel function maps the data from the model space to a high-dimensional feature space, to which many Euclidean algorithms can be generalized. The proposed method is evaluated on face recognition tasks. Comparisons with several state-of-the-art FRIS methods are performed on ChokePoint and CMU MoBo video database. The proposed methods have demonstrated promising performance.
Keywords: support vector domain description (SVDD), graph embedding, discriminant analysis, kernel method, face recognition
