Abstract:
Low resolution (LR) is an important issue when handling real world face recognition problems. The performance of traditional recognition algorithms will drop drastically ...Show MoreMetadata
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.
Published in: 2011 International Joint Conference on Biometrics (IJCB)
Date of Conference: 11-13 October 2011
Date Added to IEEE Xplore: 29 December 2011
ISBN Information: