Abstract
As one of the most important soft biometrics, gender has substantial applications in various areas such as demography and human-computer interaction. Successful gender estimation of face images taken under real-world also contributes to improving the face identification results in the wild. However, most existing gender classification methods estimate gender under well controlled environment, which limits its implementation in real-world applications. In this paper, we propose a new network architecture to combine the coarse appearance features with delicate facial features for gender estimation task. We call this method “coarse and fine” to give a harsh description of the gender estimation process. Trained on the large scale uncontrolled CelebA dataset without any alignment, the proposed network tries to learn how to estimate gender of real-world face images. Cross-database experiments on LFWA and CASIA-WebFace dataset show the superiority of our proposed method.
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References
Gutta, S., Wechsler, H.: Gender and ethnic classification of human faces using hybrid classifiers. In: Proceedings of the International Joint Conference on Neural Networks, pp. 4084–4089 (1999)
Jain, A., Huang, J., Fang, S.: Gender identification using frontal facial images. In: Proceedings of the IEEE International Conference on Multimedia and Expo, p. 4 (2005)
Baluja, S., Rowley, H.: Boosting sex identification performance. Int. J. Comput. Vis. 71(1), 111–119 (2007)
Benabdelkader, C., Griffin, P.: A local region-based approach to gender classification from face images. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, p. 52 (2005)
Toews, M., Arbel, T.: Detection, localization, and sex classification of faces from arbitrary viewpoints and under occlusion. IEEE Trans. Pattern Anal. Mach. Intell. 31(9), 1567–1581 (2009)
Ullah, I., Hussain, M., Muhammad, G., Aboalsamh, H.: Gender recognition from face images with local WLD descriptor. In: Proceedings of the International Conference on Systems, Signals and Image Processing, pp. 417–420 (2013)
Perez, C., Tapia, J., Estvez, P., Held, C.: Gender classification from face images using mutual information and feature fusion. Int. J. Optomechatronics 6(1), 92–119 (2012)
O’Toole, A., Vetter, T., Troje, N., Blthoff, H.: Sex classification is better with three-dimensional head structure than with image intensity information. Perception 26(1), 75–84 (1997)
Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.J.: The FERET database and evaluation procedure for face-recognition algorithms. Image Vis. Comput. 16(5), 295–306 (1998)
Shan, C.: Learning local binary patterns for gender classification on real-world face images. Pattern Recogn. Lett. 33(4), 431–437 (2012)
Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical report 07–49, University of Massachusetts (2007)
Makinen, E., Raisamo, R.: Evaluation of gender classification methods with automatically detected and aligned faces. IEEE Trans. Pattern Anal. Mach. Intell. 30(3), 541–547 (2008)
Reid, D.A., Samangooei, S., Chen, C., Nixon, M.S., Ross, A.: Soft biometrics for surveillance: an overview. Mach. Learn.: Theory Appl. 327–352 (2013). Elsevier
Golomb, B.A., Lawrence, D.T., Sejnowski, T.J.: SEXNET: a neural network identifies sex from human faces. NIPS 1, 2 (1990)
Poggio, B., Brunelli, R., Poggio, T.: HyberBF networks for gender classification. In: NIPS, vol. 2 (1990)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Proceedings of the Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Verma, A., Vig, L.: Using convolutional neural networks to discover cogntively validated features for gender classification. In: Proceedings of the International Conference on Soft Computing and Machine Intelligence, pp. 33–37 (2014)
Eidinger, E., Enbar, R., Hassner, T.: Age and gender estimation of unfiltered faces. IEEE Trans. Inf. Forensics Secur. 9(12), 2170–2179 (2014)
Levi, G., Hassner, T.: Age and gender classification using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 34–42 (2015)
Mansanet, J., Albiol, A., Paredes, R.: Local deep neural networks for gender recognition. Pattern Recogn. Lett. 70, 80–86 (2016)
Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3730–3738 (2015)
Yi, D., Lei, Z., Liao, S., Li, S.Z.: Learning face representation from scratch. arXiv preprint arXiv:1411.7923 (2014)
Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Sig. Process. Lett. 23(10), 1499–1503 (2016)
Acknowledgments
This work is supported by National Key Research and Development Program of China (2017YFB0802303, 2016YFC0801100) and the National Key Scientific Instrument and Equipment Development Projects of China (2013YQ49087904).
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Jiang, Q., Shao, L., Liu, Z., Zhao, Q. (2017). Coarse and Fine: A New Method for Gender Classification in the Wild. In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_18
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DOI: https://doi.org/10.1007/978-3-319-69923-3_18
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