Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/24646
Title: Elastic block set reconstruction for face recognition
Authors: Li, D
Xie, XD
Lam, KM 
Jin, Z
Keywords: Face recognition
Elastic block set reconstruction (EBSR)
Elastic local reconstruction (ELR)
Issue Date: 2009
Publisher: IEEE
Source: 2009 16th IEEE International Conference on Image Processing (ICIP), 7-10 November 2009, Cairo, p. 3329-3332 How to cite?
Abstract: In this paper, a novel face recognition algorithm named elastic block set reconstruction (EBSR) is proposed. In our method, the EBSR face is used to represent a set of training faces and to simulate different factors in a query image. An EBSR face is constructed by using the blocks from the training face images which best match to the blocks of the query image at the corresponding locations. The elastic local reconstruction (ELR) error is then used to evaluate how well a block pair matches, and the query image is classified based on the accumulated reconstruction error. The proposed method can effectively explore local information in the training set and deal with various conditions well. Also, the reconstruction error can be considered as a kind of dissimilarity measure, which gives a new approach to designing the training set so as to maximize robustness of recognition. Experiments show that consistent and promising results are obtained.
URI: http://hdl.handle.net/10397/24646
ISBN: 978-1-4244-5653-6
978-1-4244-5655-0 (E-ISBN)
ISSN: 1522-4880
DOI: 10.1109/ICIP.2009.5413936
Appears in Collections:Conference Paper

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