Low-resolution face recognition via Simultaneous Discriminant Analysis

Authors:
Changtao Zhou; Zhiwei Zhang; Dong Yi;Zhen Lei;Stan Z. Li
Affiliations:
Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China;Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Venue:
IJCB '11 Proceedings of the 2011 International Joint Conference on Biometrics
Year:
2011

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Abstract

Low resolution (LR) is an important issue when handling real world face recognition problems. The performance of traditional recognition algorithms will drop drastically due to the loss of facial texture information in original high resolution (HR) images. To address this problem, in this paper we propose an effective approach named Simultaneous Discriminant Analysis (SDA). SDA learns two mappings from LR and HR images respectively to a common subspace where discrimination property is maximized. In SDA, (1) the data gap between LR and HR is reduced by mapping into a common space; and (2) the mapping is designed for preserving most discriminative information. After that, the conventional classification method is applied in the common space for final decision. Extensive experiments are conducted on both FERET and Multi-PIE, and the results clearly show the superiority of the proposed SDA over state-of-the-art methods.