Abstract
Gender is possibly the most common facial attribute automatically estimated from images. Achieving robust gender classification “in the wild,” i.e. in images acquired in real settings, is still an open problem. Face pose variations are a major source of classification errors. They are solved using sophisticated face alignment algorithms that are costly computationally. They are also prone to getting stuck in local minima thus providing a poor pose invariance. In this paper we move the alignment problem to the learning stage. The result is an efficient pose-aware classifier with no on-line alignment. Our efficient procedure gets state of the art performance even with facial poses “in the wild.” In our experiments using “The Images of Groups” database we prove that by simultaneously predicting gender and pose we get an increase of about 5% in the performance of a linear state-of-the-art gender classifier.
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References
Babenko, B., Dollár, P., Tu, Z., Belongie, S.: Simultaneous Learning and Alignment: Multi-Instance and Multi-Pose Learning. In: Workshop on Faces in ‘Real-Life’ Images: Detection, Alignment, and Recognition, Marseille, France (2008)
Baluja, S., Rowley, H.A.: Boosting sex identification performance. International Journal of Computer Vision 71(1) (January 2007)
Bekios-Calfa, J., Buenaposada, J.M., Baumela, L.: Revisiting linear discriminant techniques in gender recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(4), 858–864 (2011)
Bourdev, L., Malik, J.: Poselets: Body part detectors trained using 3d human pose annotations. In: Proc. of International Conference on Computer Vision, pp. 1365–1372 (October 2009)
Dago-Casas, P., Gonzalez-Jimenez, D., Yu, L.L., Alba-Castro, J.: Single- and cross- database benchmarks for gender classification under unconstrained settings. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 2152–2159 (November 2011)
Gallagher, A.C., Chen, T.: Understanding images of groups of people. In: Proc. of Int. Conference on Computer Vision and Pattern Recognition, pp. 256–263 (2009)
Guo, G., Dyer, C.R., Fu, Y., Huang, T.S.: Is gender recognition affected by age? In: Proc. of IEEE International Workshop on Human-Computer Interaction (HCI 2009), pp. 2032–2039 (2009)
Guo, G., Mu, G.: A study of large-scale ethnicity estimation with gender and age variations. In: IEEE Int. Workshop on Analysis and Modeling of Faces and Gestures (AMFG 2010), pp. 79–86 (2010)
Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Tech. Rep. 07-49, University of Massachusetts, Amherst (October 2007)
Kim, T., Stenger, B., Woodley, T., Cipolla, R.: Online multiple classifier boosting for object tracking. In: Proc. IEEE Workshops on Computer Vision and Pattern Recognition (2010)
Koestinger, M., Wohlhart, P., Roth, P.M., Bischof, H.: Annotated facial landmarks in the wild: A large-scale, real-world database for facial landmark localization. In: First IEEE International Workshop on Benchmarking Facial Image Analysis Technologies (2011)
Learned-Miller, E.: Data driven image models through continuous joint alignment. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(2), 236–250 (2006)
Mäkinen, E., Raisamo, R.: Evaluation of gender classification methods with automatically detected and aligned faces. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(3), 541–547 (2008)
Moghaddam, B., Yang, M.H.: Learning gender with support faces. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 707–711 (2002)
Phillips, P., Moon, H., Rauss, P., Rizvi, S.: The feret evaluation methodology for face recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)
Toews, M., Arbel, T.: Detection, localization, and sex classification of faces from arbitrary viewpoints and under occlusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(9), 1567–1581 (2009)
Tsoumakas, G., Katakis, I., Vlahavas, I.: Random k-labelsets for multi-label classification. IEEE Transactions on Konwledge and Data Engineering (2010)
Zhu, M., Martinez, A.: Subclass discriminant analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(8), 1274–1286 (2006)
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Bekios-Calfa, J., Buenaposada, J.M., Baumela, L. (2013). Alignment-Free Gender Recognition in the Wild. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds) Pattern Recognition and Image Analysis. IbPRIA 2013. Lecture Notes in Computer Science, vol 7887. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38628-2_45
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DOI: https://doi.org/10.1007/978-3-642-38628-2_45
Publisher Name: Springer, Berlin, Heidelberg
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