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Gender Recognition from Face Images Using a Fusion of SVM Classifiers

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Image Analysis and Recognition (ICIAR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9730))

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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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  1. Marquardt Beauty Analysis. Face variations by sex (2014).

  2. 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)

    Article  Google Scholar 

  3. Moghaddam, B., Yang, M.-H.: Learning gender with support faces. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 707–711 (2002)

    Article  Google Scholar 

  4. Alexandre, L.A.: Gender recognition: a multiscale decision fusion approach. Pattern Recognit. Lett. 31(11), 1422–1427 (2010)

    Article  Google Scholar 

  5. 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)

    Google Scholar 

  6. Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)

    Article  Google Scholar 

  7. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  8. Brunelli, R., Poggio, T.: Face recognition: features versus templates. IEEE Trans. Pattern Anal. Mach. Intell. 10, 1042–1052 (1993)

    Article  Google Scholar 

  9. Baluja, S., Rowley, H.A.: Boosting sex identification performance. Int. J. Comput. Vis. 71(1), 111–119 (2007)

    Article  Google Scholar 

  10. 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)

    Article  Google Scholar 

  11. 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)

    Article  MATH  Google Scholar 

  12. 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)

    Chapter  Google Scholar 

  13. 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)

    Article  Google Scholar 

  14. 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)

    Chapter  Google Scholar 

  15. 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)

    Google Scholar 

  16. 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)

    Article  Google Scholar 

  17. Mivia Lab University of Salerno. Gender-FERET dataset (2016).

  18. Karlsruhe Insitute of Technology. Befit - benchmarking facial image analysis technologies (2011).

  19. Face++. Leading face recognition on cloud (2014).

  20. Luxand. Facial feature detection technologies (2015).

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Correspondence to Antonio Greco .

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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.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41500-0

  • Online ISBN: 978-3-319-41501-7

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