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Proceedings Paper

Face recognition based on logarithmic local binary patterns
Author(s): Debashree Mandal; Karen Panetta; Sos Agaian
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Paper Abstract

This paper presents a novel approach to the problem of face recognition that combines the classical Local Binary Pattern (LBP) feature descriptors with image processing in the logarithmic domain and the human visual system. Particularly, we have introduced parameterized logarithmic image processing (PLIP) operators based LBP feature extractor. We also use the human visual system based image decomposition, which is based on the Weber's law to extract features from the decomposed images and combine those with the features extracted from the original images thereby enriching the feature vector set and obtaining improved rates of recognition. Comparisons with other methods are also presented. Extensive experiments clearly show the superiority of the proposed scheme over LBP feature descriptors. Recognition rates as high as 99% can be achieved as compared to the recognition rate of 96.5% achieved by the classical LBP using the AT&T Laboratories face database.

Paper Details

Date Published: 19 February 2013
PDF: 12 pages
Proc. SPIE 8655, Image Processing: Algorithms and Systems XI, 865514 (19 February 2013); doi: 10.1117/12.1000250
Show Author Affiliations
Debashree Mandal, Tufts Univ. (United States)
Karen Panetta, Tufts Univ. (United States)
Sos Agaian, The Univ. of Texas at San Antonio (United States)


Published in SPIE Proceedings Vol. 8655:
Image Processing: Algorithms and Systems XI
Karen O. Egiazarian; Sos S. Agaian; Atanas P. Gotchev, Editor(s)

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