Abstract
Gender recognition in videos is a challenging task that has received limited attention in recent years. To tackle this problem, we propose to explore the use of intermediate features of a Convolutional Neural Network (CNN) with a component-based face representation methodology. With this approach we intend to exploit the gender information provided by different face parts. The features extracted from video key frames are combined with two different strategies to preserve the temporal information, and Random Forest classifiers are employed to obtain a final gender prediction for a video sequence. Our results on the McGill and COX datasets show that our proposal outperforms the end-to-end CNN approach and, in the McGill dataset, 100% of accuracy was obtained.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
Becerra-Riera, F., Méndez-Vázquez, H., Morales-González, A., Tistarelli, M.: Age and gender classification using local appearance descriptors from facial components. In: International Joint Conference on Biometrics (IJCB), pp. 799–804. IEEE (2017)
Castrillón-Santana, M., Lorenzo-Navarro, J., Ramón-Balmaseda, E.: Descriptors and regions of interest fusion for in- and cross-database gender classification in the wild. Image Vis. Comput. 57(C), 15–24 (2017)
Castrillón-Santana, M., Marsico, M.D., Nappi, M., Riccio, D.: MEG: texture operators for multi-expert gender classification. Comput. Vis. Image Underst. 156, 4–18 (2017)
Chang, L., Rodés, I., Méndez, H., del Toro, E.: Best-shot selection for video face recognition using FPGA. In: Ruiz-Shulcloper, J., Kropatsch, W.G. (eds.) CIARP 2008. LNCS, vol. 5197, pp. 543–550. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85920-8_66
Demirkus, M., Precup, D., Clark, J.J., Arbel, T.: Hierarchical spatio-temporal probabilistic graphical model with multiple feature fusion for binary facial attribute classification in real-world face videos. IEEE Trans. Pattern Anal. Mach. Intell. 38(6), 1185–1203 (2016)
Demirkus, M., Toews, M., Clark, J.J., Arbel, T.: Gender classification from unconstrained video sequences. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, pp. 55–62, June 2010
Demirkus, M., Precup, D., Clark, J.J., Arbel, T.: Hierarchical temporal graphical model for head pose estimation and subsequent attribute classification in real-world videos. Comput. Vis. Image Underst. 136, 128–145 (2015)
Huang, Z., et al.: A benchmark and comparative study of video-based face recognition on COX face database. IEEE Trans. Image Process. 24(12), 5967–5981 (2015)
Levi, G., Hassner, T.: Age and gender classification using convolutional neural networks. In: Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 34–42. IEEE (2015)
Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: International Conference on Computer Vision (ICCV), pp. 3730–3738. IEEE (2015)
Mansanet, J., Albiol, A., Paredes, R.: Local deep neural networks for gender recognition. Pattern Recognit. Lett. (PRLetters) 70(C), 80–86 (2016)
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 (LNAI), vol. 7458, pp. 335–346. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32695-0_31
Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. In: BMVC, vol. 1, p. 6 (2015)
Rodríguez, P., Cucurull, G., Gonfaus, J.M., Roca, F.X., González, J.: Age and gender recognition in the wild with deep attention. Pattern Recognit. (PR) 72(C), 563–571 (2017)
Rothe, R., Timofte, R., Gool, L.V.: Deep expectation of real and apparent age from a single image without facial landmarks. Int. J. Comput. Vis. (IJCV) 126, 144–157 (2016)
Selim, M., Sundararajan, S., Pagani, A., Stricker, D.: Image quality-aware deep networks ensemble for efficient gender recognition in the wild. In: Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018), vol. 5 (2018)
Shakhnarovich, G., Viola, P.A., Moghaddam, B.: A unified learning framework for real time face detection and classification. In: Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, FGR 2002, p. 16. IEEE Computer Society (2002)
Wang, W.C., Hsu, R.Y., Huang, C.R., Syu, L.Y.: Video gender recognition using temporal coherent face descriptor. In: 16th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2015, pp. 113–118. IEEE (2015)
Wang, X., Guo, R., Kambhamettu, C.: Deeply-learned feature for age estimation. In: Winter Conference on Applications of Computer Vision (WACV), pp. 534–541 (2015)
Zhong, Y., Sullivan, J., Li, H.: Face attribute prediction using off-the-shelf deep learning networks. In: International Conference on Biometrics (ICB). IEEE (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Becerra-Riera, F., Morales-González, A., Méndez-Vázquez, H. (2018). Exploring Local Deep Representations for Facial Gender Classification in Videos. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2018. Lecture Notes in Computer Science(), vol 11047. Springer, Cham. https://doi.org/10.1007/978-3-030-01132-1_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-01132-1_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01131-4
Online ISBN: 978-3-030-01132-1
eBook Packages: Computer ScienceComputer Science (R0)