Restricted Boltzmann Machines for Gender Classification

This paper deals with automatic feature learning using a generative model called Restricted Boltzmann Machine (RBM) for the problem of gender recognition in face images. The RBM is presented together with some practical learning tricks to improve the learning capabilities and speedup the training process. The performance of the features obtained is compared against several linear methods using the same dataset and the same evaluation protocol. The results show a classification accuracy improvement compared with classical linear projection methods. Moreover, in order to increase even more the classification accuracy, we have run some experiments where an SVM is fed with the non-linear mapping obtained by the RBM in a tandem configuration.Mansanet Sandin, J.; Albiol Colomer, A.; Paredes Palacios, R.; Villegas, M.; Albiol Colomer, AJ. (2014). Restricted Boltzmann Machines for Gender Classification. Lecture Notes in Computer Science. 8814:274-281. doi:10.1007/978-3-319-11758-4_30S2742818814Bengio, Y., Courville, A., Vincent, P.: Representation learning: A review and new perspectives. IEEE Trans. on PAMI 35(8), 1798–1828 (2013)Bressan, M., Vitrià, J.: Nonparametric discriminant analysis and nearest neighbor classification. Pattern Recognition Letters 24(15), 2743–2749 (2003)Buchala, S., et al.: Dimensionality reduction of face images for gender classification. In: Proceedings of the Intelligent Systems, vol. 1, pp. 88–93 (2004)Cai, D., He, X., Hu, Y., Han, J., Huang, T.: Learning a spatially smooth subspace for face recognition. In: CVPR, pp. 1–7 (2007)Courville, A., Bergstra, J., Bengio, Y.: Unsupervised models of images by spike-and-slab rbms. In: ICML, pp. 1145–1152 (2011)Huang, G.B., et al.: Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report 07–49, Univ. of Massachusetts (October 2007)Schmah, T., et al.: Generative versus discriminative training of rbms for classification of fmri images. In: NIPS, pp. 1409–1416 (2008)Graf, A.B.A., Wichmann, F.A.: Gender classification of human faces. In: Bülthoff, H.H., Lee, S.-W., Poggio, T.A., Wallraven, C. (eds.) BMCV 2002. LNCS, vol. 2525, pp. 491–500. Springer, Heidelberg (2002)He, X., Niyogi, P.: Locality preserving projections. In: NIPS (2004)Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Comput. 14(8), 1771–1800 (2002)Hinton, G.E.: A practical guide to training restricted boltzmann machines. Technical report, University of Toronto (2010)Hinton, G.E., Salakhutdinov, R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)Moghaddam, B., Yang, M.-H.: Learning gender with support faces. IEEE Trans. on PAMI 24(5), 707–711 (2002)Nair, V., Hinton, G.E.: 3d object recognition with deep belief nets. In: NIPS, pp. 1339–1347 (2009)Salakhutdinov, R., Mnih, A., Hinton, G.: Restricted boltzmann machines for collaborative filtering. In: ICML, pp. 791–798 (2007)Shan, C.: Learning local binary patterns for gender classification on real-world face images. Pattern Recognition Letters 33(4), 431–437 (2012)Shobeirinejad, A., Gao, Y.: Gender classification using interlaced derivative patterns. In: ICPR, pp. 1509–1512 (2010)Villegas, M., Paredes, R.: Dimensionality reduction by minimizing nearest-neighbor classification error. Pattern Recognition Letters 32(4), 633–639 (2011

The Role of Global and Feature Based Information in Gender Classification of Faces: A Comparison of Human Performance and Computational Models

Peer reviewe

Improving face gender classification by adding deliberately misaligned faces to the training data

A novel method of face gender classifier construction is proposed and evaluated. Previously, researchers have assumed that a computationally expensive face alignment step (in which the face image is transformed so that facial landmarks such as the eyes, nose, chin, etc, are in uniform locations in the image) is required in order to maximize the accuracy of predictions on new face images. We, however, argue that this step is not necessary, and that machine learning classifiers can be made robust to face misalignments by automatically expanding the training data with examples of faces that have been deliberately misaligned (for example, translated or rotated). To test our hypothesis, we evaluate this automatic training dataset expansion method with two types of image classifier, the first based on weak features such as Local Binary Pattern histograms, and the second based on SIFT keypoints. Using a benchmark face gender classification dataset recently proposed in the literature, we obtain a state-of-the-art accuracy of 92.5%, thus validating our approach