NDLI: Single-Sample Face Recognition with Image Corruption and Misalignment via Sparse Illumination Transfer

Content Provider	IEEE Xplore Digital Library
Author	Liansheng Zhuang Yang, A.Y. Zihan Zhou Sastry, S.S. Yi Ma
Copyright Year	2013
Description	Author affiliation: Univ. of Sci. & Technol. of China, Hefei, China (Liansheng Zhuang) \|\| Dept. of ECE, Univ. of Illinois, Urbana, IL, USA (Zihan Zhou; Yi Ma) \|\| Dept. of EECS, UC Berkeley, Berkeley, CA, USA (Yang, A.Y.; Sastry, S.S.)
Abstract	Single-sample face recognition is one of the most challenging problems in face recognition. We propose a novel face recognition algorithm to address this problem based on a sparse representation based classification (SRC) framework. The new algorithm is robust to image misalignment and pixel corruption, and is able to reduce required training images to one sample per class. To compensate the missing illumination information typically provided by multiple training images, a sparse illumination transfer (SIT) technique is introduced. The SIT algorithms seek additional illumination examples of face images from one or more additional subject classes, and form an illumination dictionary. By enforcing a sparse representation of the query image, the method can recover and transfer the pose and illumination information from the alignment stage to the recognition stage. Our extensive experiments have demonstrated that the new algorithms significantly outperform the existing algorithms in the single-sample regime and with less restrictions. In particular, the face alignment accuracy is comparable to that of the well-known Deformable SRC algorithm using multiple training images, and the face recognition accuracy exceeds those of the SRC and Extended SRC algorithms using hand labeled alignment initialization.
Starting Page	3546
Ending Page	3553
File Size	591843
Page Count	8
File Format	PDF
ISBN	9780769549897
ISSN	10636919
DOI	10.1109/CVPR.2013.455
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lighting Face Training Dictionaries Face recognition Image recognition Robustness face alignment Single-sample face recognition sparse representation image corruption
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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