Face feature extraction and recognition via local binary pattern and two-dimensional locality preserving projection
|dc.identifier.citation||Zhou, L. and Wang, H. and Liu, W. and Lu, Z. 2018. Face feature extraction and recognition via local binary pattern and two-dimensional locality preserving projection. Multimedia Tools and Applications. 78 (11): pp. 14971–14987.|
In this paper, we propose a novel face feature extraction approach based on Local Binary Pattern (LBP) and Two Dimensional Locality Preserving Projections (2DLPP) to enhance the texture features and preserve the space structure properties of a face image. LBP is firstly used to remove the effect of illumination and noise, which would enhance the detailed texture characteristics of face images. Then 2DLPP is performed to extract some prominent features and decrease the image dimension with space structure information. The Nearest Neighborhood Classifier (NNC) is used to recognize a face image at the end. In addition, the rule for dimension selection is studied from the results of experiments about choosing an appropriate feature dimension by 2DLPP computation. The experimental results on the Yale, the extended Yale B and CMU PIE C09 benchmark datasets showed that the proposed face feature extraction and recognition method achieves a better performance in comparison with similar techniques, and the proposed dimension selection rule can give an appropriate feature dimension in 2DLPP.
|dc.title||Face feature extraction and recognition via local binary pattern and two-dimensional locality preserving projection|
|dcterms.source.title||Multimedia Tools and Applications|
|curtin.department||School of Electrical Engineering, Computing and Mathematical Science (EECMS)|
|curtin.accessStatus||Fulltext not available|
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