Abstract
The recognition of gender from face images is an important application, especially in the fields of security, marketing and intelligent user interfaces. We propose an approach to gender recognition from faces by fusing the decisions of SVM classifiers. Each classifier is trained with different types of features, namely HOG (shape), LBP (texture) and raw pixel values. For the latter features we use an SVM with a linear kernel and for the two former ones we use SVMs with histogram intersection kernels. We come to a decision by fusing the three classifiers with a majority vote. We demonstrate the effectiveness of our approach on a new dataset that we extract from FERET. We achieve an accuracy of 92.6 %, which outperforms the commercial products Face++ and Luxand.
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References
Marquardt Beauty Analysis. Face variations by sex (2014). http://www.beautyanalysis.com/beauty-and-you/face-variations/face-variations-sex/
Perrett, D.I., Rolls, E.T., Caan, W.: Visual neurones responsive to faces in the monkey temporal cortex. Exp. Brain Res. 47(3), 329–342 (1982)
Moghaddam, B., Yang, M.-H.: Learning gender with support faces. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 707–711 (2002)
Alexandre, L.A.: Gender recognition: a multiscale decision fusion approach. Pattern Recognit. Lett. 31(11), 1422–1427 (2010)
Sun, Y., Wang, X., Tang, X.: Deep convolutional network cascade for facial point detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3476–3483 (2013)
Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Brunelli, R., Poggio, T.: Face recognition: features versus templates. IEEE Trans. Pattern Anal. Mach. Intell. 10, 1042–1052 (1993)
Baluja, S., Rowley, H.A.: Boosting sex identification performance. Int. J. Comput. Vis. 71(1), 111–119 (2007)
Yang, J., Zhang, D., Frangi, A.F., Yang, J.-Y.: Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26(1), 131–137 (2004)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Lian, H.-C., Lu, B.-L.: Multi-view gender classification using local binary patterns and support vector machines. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3972, pp. 202–209. Springer, Heidelberg (2006)
Tapia, J.E., Perez, C.A.: Gender classification based on fusion of different spatial scale features selected by mutual information from histogram of LBP, intensity, shape. IEEE Trans. Inf. Forensics Secur. 8(3), 488–499 (2013)
Milborrow, S., Nicolls, F.: Locating facial features with an extended active shape model. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 504–513. Springer, Heidelberg (2008)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)
Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1090–1104 (2000)
Mivia Lab University of Salerno. Gender-FERET dataset (2016). http://mivia.unisa.it/database/gender-feret.zip
Karlsruhe Insitute of Technology. Befit - benchmarking facial image analysis technologies (2011). http://fipa.cs.kit.edu/412.php
Face++. Leading face recognition on cloud (2014). http://www.faceplusplus.com/
Luxand. Facial feature detection technologies (2015). https://www.luxand.com/
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Azzopardi, G., Greco, A., Vento, M. (2016). Gender Recognition from Face Images Using a Fusion of SVM Classifiers. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_59
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DOI: https://doi.org/10.1007/978-3-319-41501-7_59
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