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Face Image Retrieval Using Sparse Representation Classifier with Gabor-LBP Histogram
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- Authors
- Han Sung Lee, Yun Su Chung, Jeong Nyeo Kim, Dai Hee Park
- Issue Date
- 2010-08
- Citation
- International Workshop on Information Security Applications (WISA) 2010 (LNCS 6513), v.6513, pp.273-280
- Publisher
- Springer
- Language
- English
- Type
- Conference Paper
- 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. © 2011 Springer-Verlag.
- KSP Keywords
- Accuracy Rate, Face image retrieval, Geometrical configuration, High accuracy, LBP histogram, Surveillance system, face image representation, facial features, practical application, retrieval accuracy, retrieval method