Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30761
Title: Sparse variation dictionary learning for face recognition with a single training sample per person
Authors: Yang, M
Gool, LV
Zhang, L 
Keywords: Face recognition
Image representation
Learning (artificial intelligence)
Issue Date: 2013
Publisher: IEEE
Source: 2013 IEEE International Conference on Computer Vision (ICCV), 1-8 December 2013, Sydney, NSW, p. 689-696 How to cite?
Abstract: Face recognition (FR) with a single training sample per person (STSPP) is a very challenging problem due to the lack of information to predict the variations in the query sample. Sparse representation based classification has shown interesting results in robust FR, however, its performance will deteriorate much for FR with STSPP. To address this issue, in this paper we learn a sparse variation dictionary from a generic training set to improve the query sample representation by STSPP. Instead of learning from the generic training set independently w.r.t. the gallery set, the proposed sparse variation dictionary learning (SVDL) method is adaptive to the gallery set by jointly learning a projection to connect the generic training set with the gallery set. The learnt sparse variation dictionary can be easily integrated into the framework of sparse representation based classification so that various variations in face images, including illumination, expression, occlusion, pose, etc., can be better handled. Experiments on the large-scale CMU Multi-PIE, FRGC and LFW databases demonstrate the promising performance of SVDL on FR with STSPP.
URI: http://hdl.handle.net/10397/30761
ISBN: 
ISSN: 1550-5499
DOI: 10.1109/ICCV.2013.91
Appears in Collections:Conference Paper

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