Face image retrieval using sparse representation classifier with gabor-LBP histogram

Authors:
Hansung Lee;Yunsu Chung;Jeongnyeo Kim;Daihee Park
Affiliations:
Electronics and Telecommunications Research Institute, Korea;Electronics and Telecommunications Research Institute, Korea;Electronics and Telecommunications Research Institute, Korea;Dept. of Computer and Information Science, Korea University, Korea
Venue:
WISA'10 Proceedings of the 11th international conference on Information security applications
Year:
2010

Citing 7
Cited 3

Face recognition: A literature survey

ACM Computing Surveys (CSUR)
Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A Novel Non-Statistical Model for Face Representation and Recognition

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A review on Gabor wavelets for face recognition

Pattern Analysis & Applications
Face Indexing and Retrieval by Spatial Similarity

CISP '08 Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 1 - Volume 01
Robust Face Recognition via Sparse Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparsity preserving projections with applications to face recognition

Pattern Recognition
Fusing local patterns of gabor magnitude and phase for face recognition

IEEE Transactions on Image Processing

Novel color Gabor-LBP-PHOG (GLP) descriptors for object and scene image classification

Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Novel Gabor-PHOG features for object and scene image classification

SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
New color GPHOG descriptors for object and scene image classification

Machine Vision and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Face image retrieval is an important issue in the practical applications such as mug shot searching and surveillance systems. However, it is still a challenging problem because face images are fairly similar due to the same geometrical configuration of facial features. In this paper, we present a face image retrieval method which is robust to the variations of face image condition and with high accuracy. Firstly, we choose the Gabor-LBP histogram for face image representation. Secondly, we use the sparse representation classification for the face image retrieval. Using the Gabor-LBP histogram and sparse representation classifier, we achieved effective and robust retrieval results with high accuracy. Finally, experiments are conducted on ETRI and XM2VTS database to verify a proposed method. It showed rank 1 retrieval accuracy rate of 98.9% on ETRI face set, and of 99.3% on XM2VTS face set, respectively.