Multi-view Gender Classification Using Local Binary Patterns and Support Vector Machines
In this paper, we present a novel approach to multi-view gender classification considering both shape and texture information to represent facial image. The face area is divided into small regions, from which local binary pattern(LBP) histograms are extracted and concatenated into a single vector efficiently representing the facial image. The classification is performed by using support vector machines(SVMs), which had been shown to be superior to traditional pattern classifiers in gender classification problem. The experiments clearly show the superiority of the proposed method over support gray faces on the CAS-PEAL face database and a highest correct classification rate of 96.75% is obtained. In addition, the simplicity of the proposed method leads to very fast feature extraction, and the regional histograms and global description of the face allow for multi-view gender classification.
KeywordsSupport Vector Machine Facial Image Local Binary Pattern Local Binary Pattern Operator Grey Pixel
Unable to display preview. Download preview PDF.
- Balci, K., Atalay, V.: PCA for Gender Estimation: Which Eigenvectors Contribute? In: 16th International Conference on Pattern Recognition (ICPR 2002), Quebec City, QC, Canada, vol. 3, pp. 363–366 (2002)Google Scholar
- Iga, R.: Gender and Age Estimation System from Face Images. In: SICE Annual Conference in Fukui, August 4-6, pp. 756–761 (2003)Google Scholar
- Hosoi, S., Takikawa, E., Kawade, M.: Ethnicity Estimation with Facial Images. In: Sixth IEEE International Conference on Automatic Face and Gesture Recognition, Seoul, Korea, May 17-19, pp. 195–200 (2004)Google Scholar
- Jin, H.L., Liu, Q.S., Tong, X.F.: Face Detection Using Improved LBP Under Bayesian Framework. In: Proceedings of the Third International Conference on Image and Graphics (ICIG 2004), Hong Kong, China, December 18-20, pp. 306–309 (2004)Google Scholar
- Ahonen, T., Hadid, A., Pietikäinen, M.: Face Recognition with Local Binary Patterns. In: Proceedings of the European Conference on Computer Vision, pp. 469–481 (2004)Google Scholar
- Gao, W., Cao, B., Shan, S.G., et al.: The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations, technical report of JDL (2004), avaible on, http://www.jdl.ac.cn/~peal/peal_tr.pdf
- Chang, C.C., Lin, C.J.: LIBSVM: a Library for Support Vector Machines, http://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.ps.gz