NDLI: Sparse Variation Dictionary Learning for Face Recognition with a Single Training Sample per Person

Content Provider	IEEE Xplore Digital Library
Author	Meng Yang Van Gool, L. Lei Zhang
Copyright Year	2013
Description	Author affiliation: ETH Zurich, Zurich, Switzerland (Meng Yang; Van Gool, L.) \|\| Hong Kong Polytech. Univ., Hong Kong, China (Lei Zhang)
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.
Sponsorship	IEEE Comput. Soc.
Starting Page	689
Ending Page	696
File Size	616276
Page Count	8
File Format	PDF
ISBN	9781479928408
ISSN	15505499
DOI	10.1109/ICCV.2013.91
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-01
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Dictionaries Training Face Lighting Databases Bismuth Encoding
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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