Affiliations: [a]
School of Management, Nanjing University of Posts and Telecommunications, Nanjing, China
| [b]
Institute of Systems Engineering, Southeast University, Nanjing, China
| [c]
Vision and Security Technology Lab, University of Colorado, Colorado Springs, USA
Correspondence:
[*]
Corresponding author. Yongjie Chu, School of Management, Nanjing University of Posts and Telecommunications, Jiangsu 211100, China. +86 13621583817; E-mail: [email protected].
Abstract: In this paper, an enhanced discriminative feature learning (EDFL) method is proposed to address single sample per person (SSPP) face recognition. With a separate auxiliary dataset, EDFL integrates Fisher discriminative learning and domain adaptation into a unified framework. The separate auxiliary dataset and the gallery/probe dataset are from two different domains (named source and target domains respectively) and have different data distributions. EDFL is modeled to transfer the discriminative knowledge learned from the source domain to the target domain for classification. Since the gallery set with SSPP contains scarce number of samples, it is hard to accurately represent the data distribution of the target domain, which hinders the adaptation effect. To overcome this problem, the generalized domain adaption (GDA) method is proposed to realize good overall domain adaptation when one domain contains limited samples. GDA considers the both global and local domain adaptation effect at the same time. Further, to guarantee that the learned domain adaptation components are optimal for discriminative learning, the domain adaptation and Fisher discriminant model learning are unified into a single framework and an efficient algorithm is designed to optimize them. The effectiveness of the proposed approach is demonstrated by extensive evaluation and comparison with some state-of-the-art methods.
Keywords: Single sample per person, domain
adaptation, discriminative feature learning, feature
selection
