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Local Line Derivative Pattern For Face Recognition
Lian, Zhichao; Meng Joo Er; Cong Y(丛杨)
Conference Name2012 IEEE International Conference on Image Processing
Conference DateSeptember 30 - October 3, 2012
Conference PlaceOrland, Florida, USA
Author of SourceIEEE Signal Processing Society
Source PublicationProceedings of the 2012 IEEE International Conference on Image Processing
Publication PlaceNew York, USA
Indexed ByEI ; CPCI(ISTP)
EI Accession number20131116112575
WOS IDWOS:000319334901131
Contribution Rank2
KeywordFace Recognition High-order Local Pattern Local Binary Pattern
AbstractIn this paper, we propose a novel face descriptor for face recognition, named Local Line Derivative Pattern (LLDP). High-order derivative images in two directions are obtained by convolving original images with Sobel Masks. A revised binary coding function is proposed and three standards on arranging the weights are also proposed. Based on the standards, the weights of a line neighborhood in two directions are arranged. The LLDP labels in two directions are calculated with the proposed binary coding function and weights. The labeled image is divided into blocks where spatial histograms are extracted separately and concatenated into an entire histogram as features for recognition. The experiments on the FERET and Extended Yale B show superior performances of the proposed LLDP compared to other existing methods based on the LBP. The results prove that the LLDP has good robustness against expression, illumination and aging variations.
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Document Type会议论文
Corresponding AuthorLian, Zhichao
Affiliation1.School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Science, China
Recommended Citation
GB/T 7714
Lian, Zhichao,Meng Joo Er,Cong Y. Local Line Derivative Pattern For Face Recognition[C]//IEEE Signal Processing Society. New York, USA:IEEE,2012:1449-1452.
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