Systems and Means of Informatics

November 2013, Volume 23, Issue 2, pp 50-61

PERSON CLASSIFICATION UPON FACE IMAGE BASED ON SIMILE CLASSIFIERS

V. S. Konushin
T. M. Lukina
A. I. Kuharenko
A.S. Konushin

Abstract

This paper presents a new approach to person classification from face images. It consists of two steps. First, similarity distances between a test face and faces from a known sample are computed. For this task special simile classifiers are trained. Similarity distances are estimated independently for each face fragment, e.g. nose, mouth, left or right eye, etc. Pixel colors, brightness, gradient norm and orientation are used as features both for each pixel and for whole fragment in form of a histogram or Gaussian distribution parameters. Person classification is performed based on these similarity distances. For classification SVM with RBF kernel is used. The proposed algorithm was tested on a gender classification task, using LFW and PubFig databases. The algorithm achieved 92.96% accuracy.

[+] References (21)

Kumar, N., A.C. Berg, P. N. Belhumeur, and S.K. Nayar. 2009. Attribute and simile classifiers for face verification. IEEE International Conference on Computer Vision. NY: IEEE. 365{72.
Huang, G., M. Ramesh, T. Berg, and E. Learned-Miller. 2007. Labeled faces in the wild: A database for studying face recognition in unconstrained environments. UMass Amherst Technical Report 07-49.
Yang, Z., M. Li, and H. Ai. 2006. An experimental study on automatic face gender classification. 18th International Conference on Pattern Recognition Proceedings. NY: IEEE. 1099-102.
Mayo, M., and E. Zhang. 2008. Improving face gender classification by adding deliberately misaligned faces to the training data. IVCNZ08: 23rd International Conference Image and Vision Computing Proceedings. New Zealand. 1{5.
Moghaddam, B., and M. Yang. 2002. Learning gender with support faces. IEEE Trans. Pattern Anal. Machine Intelligence 24(5):707{11.
Baluja, S., and H.A. Rowley. 2007. Boosting sex identification performance. Int. J. Comput. Vision 71(1):111-19.
Makinen, E., and R. Raisamo. 2008. Evaluation of gender classification methods with automatically detected and aligned faces. IEEE Trans. Pattern Anal. Machine Intelligence 30:541-47.
Guo, G., Y. Fu, T. S. Huang, and C. Dyer. 2008. A probabilistic fusion approach to human age prediction. IEEE CVPR-SLAM Workshop. NY: IEEE. 1-6.
Chang, K.-Y., C.-S. Chen, and Y.-P. Hung. 2011. Ordinal hyperplanes ranker with cost sensitivities for age estimation. IEEE Conference on Computer Vision and Pattern Recognition. NY: IEEE. 585-92.
Lian, H.C., and B. L. Lu. 2006. Multi-view gender classification using local binary patterns and support vector machines. 5th International Conference on Artificial Neural Networks Proceedings. 2:202-9.
Shan, C. 2012. Learning local binary patterns for gender classification on real-world face images. Pattern Recognition Lett. 33(4):431-37.
Mu, G.W., G.D. Guo, Y. Fu, and T. S. Huang. 2009. Human age estimation using bio-inspired features. IEEE computer vision and pattern recognition. NY: IEEE. 112-19.
Guo, G., G. Mu, Y. Fu, C. Dyer, and T. S. Huang. 2009. A study on automatic age estimation using a large database. IEEE International Conference on Computer Vision. NY: IEEE. 1986-91.
Kumar, N., P. N. Belhumeur, and S.K. Nayar. 2008. FaceTracer: A search engine for large collections of images with faces. European Conference on Computer Vision. Berlin, Heidelberg: Springer. 340-53.
Montillo, A., and H. Ling. 2009. Age regression from faces using random forests. IEEE International Conference on Image Processing. NY: IEEE. 2465-68.
Du, P., and D. Xiaoqing. 2008. The application of decision tree in gender classification. Congress on Image and Signal Processing. Los Alamitos: IEEE Computer Society. 657-60.
Yin, Q., X. Tang, and J. Sun. 2011. An associate-predict model for face recognition. IEEE computer vision and pattern recognition. NY: IEEE. 497-504.
Viola, P., and M. J. Jones. 2004. Robust real-time face detection. Int. J. Computer Vision 57(2):137-54.
Cristinacce, D., and T. Cootes. 2007. Boosted regression active shape models. British Machine Vision Conference.Warwick. 2:880-89.
Phillips, P. J., H. Moon, S.A. Rizvi, and P. Rauss. 2000. The FERET evaluation methodology for face recognition algorithms. IEEE PAMI 22(10):1090-104.
Gao, W., B. Cao, S.G. Shan, et al. 2004. The CAS-PEAL large-scale chinese face database and baseline evaluations. Technical Report of JDL.

[+] About this article