A novel face feature descriptor using adaptively weighted extended LBP pyramid

This paper proposes a new face description with single sample by Adaptively Weighted Extended Local Binary Pattern Pyramid (AWELBPP). First, the proposed algorithm utilizes pyramid transform to represent sample image into multi-resolution images. Second, the multi-resolution images are divided into...

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Bibliographic Details
Published inOptik (Stuttgart) Vol. 124; no. 23; pp. 6286 - 6291
Main Authors Gao, Tao, Feng, X.L., Lu, He, Zhai, J.H.
Format Journal Article
LanguageEnglish
Published Elsevier GmbH 01.12.2013
Subjects
Algorithms
AWELBPP
ELBP
Entropy
Face recognition
Illumination
LBP
Mathematical analysis
Pyramids
LBP
Face recognition
ELBP
AWELBPP
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Summary:This paper proposes a new face description with single sample by Adaptively Weighted Extended Local Binary Pattern Pyramid (AWELBPP). First, the proposed algorithm utilizes pyramid transform to represent sample image into multi-resolution images. Second, the multi-resolution images are divided into a set of horizontal sub-images. Then, Extended Local Binary Pattern (ELBP) is applied to the sub-images in order to calculate the Sub-ELBP pyramid and the local image entropy is employed to the sub-images for an adaptively weighting map (AWM) that can measure the importance of the information they contain. Finally, AWELBPP feature is extracted from the fusion of the Sub-ELBP pyramid and the AWM. Under different illumination, facial expression and partial occlusion conditions, simulated experiments and comparisons on many subsets of Yale face databases, Yale B face databases and ORL face databases show that the proposed algorithm is an outstanding method for single sample face recognition compared with local PCA, ELBP, CLBP, PLBP.
Bibliography:ObjectType-Article-1
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content type line 23
ISSN:0030-4026
1618-1336
DOI:10.1016/j.ijleo.2013.05.007