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.
This research was financially supported by the Electronics and Telecommunications Research Institute (ETRI) through the project of “Development of CCTV Face Recognition and Identification Technology under Unconstrained Environment”; This research was partially supported by the Ministry of Education, Science Technology (MEST) and Korea Industrial Technology Foundation (KOTEF) through the Human Resource Training Project for Regional Innovation.
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Lee, H., Chung, Y., Kim, J., Park, D. (2011). Face Image Retrieval Using Sparse Representation Classifier with Gabor-LBP Histogram. In: Chung, Y., Yung, M. (eds) Information Security Applications. WISA 2010. Lecture Notes in Computer Science, vol 6513. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17955-6_20
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DOI: https://doi.org/10.1007/978-3-642-17955-6_20
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