Abstract
In this paper we focus on gender classification from face images, which is still a challenging task in unrestricted scenarios. This task can be useful in a number of ways, e.g., as a preliminary step in biometric identity recognition supported by demographic information. We compare a feature based approach with two score based ones. In the former, we stack a number of feature vectors obtained by different operators, and train a SVM based on them. In the latter, we separately compute the individual scores from the same operators, then either we feed them to a SVM, or exploit likelihood ratio based on a pairwise comparison of their answers. Experiments use EGA database, which presents a good balance with respect to demographic features of stored face images. As expected, feature level fusion achieves an often better classification performance but it is also quite computationally expensive. Our contribution has a threefold value: 1) the proposed score level fusion approaches, though less demanding, achieve results which are rather similar or slightly better than feature level fusion, especially when a particular set of experts are fused; since experts are trained individually, it is not required to evaluate a complex multi-feature distribution and the training process is more efficient; 2) the number of uncertain cases significantly decreases; 3) the operators used are not computationally expensive in themselves.
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Klare, B.E., Burge, M.J., Klontz, J.C., Jain, A.K., Jain, A.K.: Face recognition performance: Role of demographic information. IEEE Trans. on Information Forensics and Security 7(6), 1789–1801 (2012)
Bekios-Calfa, J., Buenaposada, J.M., Baumela, L.: Robust gender recognition by exploiting facial attributes dependencies. Pattern Recognition Letters 36, 228–234 (2014)
Ng, C.B., Tay, Y.H., Goi, B.-M.: Recognizing human gender in computer vision: a survey. In: Anthony, P., Ishizuka, M., Lukose, D. (eds.) PRICAI 2012. LNCS, vol. 7458, pp. 335–346. Springer, Heidelberg (2012)
Torralba, A., Efros, A.A.: Unbiased look at dataset bias. In: Computer Vision and Pattern Recognition (2011)
Riccio, D., Tortora, G., De Marsico, M., Wechsler, H.: EGA - ethnicity, gender and age, a pre-annotated face database. In: 2012 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BIOMS), pp. 1–8 (2012)
Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.: Computer Vision Using Local Binary Patterns. Springer (2011)
Jun, B., Kim, D.: Robust face detection using local gradient patterns and evidence accumulation. Pattern Recognition 45(9), 3304–3316 (2012)
Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. In: Zhou, S.K., Zhao, W., Tang, X., Gong, S. (eds.) AMFG 2007. LNCS, vol. 4778, pp. 168–182. Springer, Heidelberg (2007)
Zhang, B., Gao, Y., Zhao, S., Liu, J.: Local derivative pattern versus local binary pattern: Face recognition with high-order local pattern descriptor. IEEE Trans. on Image Processing 19(2), 533–544 (2010)
Chen, J., Shan, S., He, C., Zhao, G., Pietikainen, M., Chen, X., Gao, W.: Wld: A robust local image descriptor. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9), 1705–1720 (2010)
Ojansivu, V., Heikkilä, J.: Blur insensitive texture classification using local phase quantization. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2008 2008. LNCS, vol. 5099, pp. 236–243. Springer, Heidelberg (2008)
Ahonen, T., Hadid, A., Pietikäinen, M.: Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12), December 2006
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Ulery, B., Hicklin, A.R., Watson, C., Fellner, W., Hallinan, P.: Studies of biometric fusion. Technical Report IR 7346, NIST (2006)
CASIA-FaceV5. http://biometrics.idealtest.org/
The FEI face database. http://www.fei.edu.br/~cet/facedatabase.html
Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.: The FERET Database and Evaluation Procedure for Face-Recognition Algorithms. J. Image and Vision Computing 16(5), 295–306 (1988)
Phillips, P., Flynn, P., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the face recognition grand challenge. In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2005)
Lyons, M.J., Akamatsu, S., Kamachi, M., Gyoba, J.: Coding facial expressions with gabor wavelets. In: Proceeding of the IEEE International Conference on Automatic Face and Gesture Recognition, pp. 200–205 (1998)
Jain, V., Mukherjee, A.: The Indian Face Database. http://vis-www.cs.umass.edu/~vidit/IndianFaceDatabase/
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Castrillón-Santana, M., De Marsico, M., Nappi, M., Riccio, D. (2015). MEG: Multi-Expert Gender Classification from Face Images in a Demographics-Balanced Dataset. In: Murino, V., Puppo, E. (eds) Image Analysis and Processing — ICIAP 2015. ICIAP 2015. Lecture Notes in Computer Science(), vol 9279. Springer, Cham. https://doi.org/10.1007/978-3-319-23231-7_2
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