Abstract
In this work we address the problem of gender recognition from facial images acquired in the wild. This problem is particularly difficult due to the presence of variations in pose, ethnicity, age and image quality. Moreover, we consider the special case in which only a small sample size is available for the training phase. We rely on a feature representation obtained from the well known VGG-Face Deep Convolutional Neural Network (DCNN) and exploit the effectiveness of a sparse-driven sub-dictionary learning strategy which has proven to be able to represent both local and global characteristics of the train and probe faces. Results on the publicly available LFW dataset are provided in order to demonstrate the effectiveness of the proposed method.
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References
Adamo, A., Grossi, G., Lanzarotti, R., Lin, J.: Sparse decomposition by iterating lipschitzian-type mappings. Theoret. Comput. Sci. 664, 12–28 (2017)
Afifi, M., Abdelhamed, A.: Afif4: Deep gender classification based on adaboost-based fusion of isolated facial features and foggy faces. arXiv preprint arXiv:1706.04277 (2017)
Aharon, M., Elad, M., Bruckstein, A., et al.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54(11), 4311 (2006)
Boccignone, G., Conte, D., Cuculo, V., Lanzarotti, R.: Amhuse: a multimodal dataset for humour sensing. In: Proceedings of the 19th ACM International Conference on Multimodal Interaction, pp. 438–445. ACM (2017)
Bodini, M., D’Amelio, A., Grossi, G., Lanzarotti, R., Lin, J.: Single sample face recognition by sparse recovery of deep-learned LDA features. In: Blanc-Talon, J., Helbert, D., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2018. LNCS, vol. 11182, pp. 297–308. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01449-0_25
Cuculo, V., D’Amelio, A., Grossi, G., Lanzarotti, R., Lin, J.: Robust single-sample face recognition by sparsity-driven sub-dictionary learning using deep features. Sensors 19(1), 146 (2019)
Dago-Casas, P., González-Jiménez, D., Yu, L.L., Alba-Castro, J.L.: 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. IEEE (2011)
Engan, K., Aase, S.O., Husoy, J.H.: Method of optimal directions for frame design. In: 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No. 99CH36258), vol. 5, pp. 2443–2446. IEEE (1999)
Grossi, G., Lanzarotti, R., Lin, J.: Orthogonal procrustes analysis for dictionary learning in sparse linear representation. PLoS ONE 12(1), e0169663 (2017)
Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database forstudying face recognition in unconstrained environments. In: Workshop on faces in ‘Real-Life’ Images: Detection, Alignment and Recognition (2008)
Martinez, A.M.: The AR face database. CVC Technical Report24 (1998)
Ng, C.-B., Tay, Y.-H., Goi, B.-M.: A review of facial gender recognition. Pattern Anal. Appl. 18(4), 739–755 (2015). https://doi.org/10.1007/s10044-015-0499-6
Parkhi, O.M., Vedaldi, A., Zisserman, A., et al.: Deep face recognition. In: BMVC, vol. 1, no. 6 (2015)
Pati, Y.C., Rezaiifar, R., Krishnaprasad, P.S.: Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In: Proceedings of 27th Asilomar Conference on Signals, Systems and Computers, pp. 40–44. IEEE (1993)
Ranjan, R., Patel, V.M., Chellappa, R.: Hyperface: a deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. IEEE Trans. Pattern Anal. Mach. Intell. 41(1), 121–135 (2019)
Shan, C.: Learning local binary patterns for gender classification on real-world face images. Pattern Recogn. Lett. 33(4), 431–437 (2012)
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, and shape. IEEE Trans. Inf. Forensics Secur. 8(3), 488–499 (2013)
Zhang, Z., Xu, Y., Yang, J., Li, X., Zhang, D.: A survey of sparse representation: algorithms and applications. IEEE access 3, 490–530 (2015)
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D’Amelio, A., Cuculo, V., Bursic, S. (2020). Gender Recognition in the Wild with Small Sample Size - A Dictionary Learning Approach. In: Sekerinski, E., et al. Formal Methods. FM 2019 International Workshops. FM 2019. Lecture Notes in Computer Science(), vol 12232. Springer, Cham. https://doi.org/10.1007/978-3-030-54994-7_12
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