Abstract
To estimate multiple face attributes, independent classifier for each attribute are trained such as facial point detection, gender recognition, and age estimation in the conventional approach. It is inefficient because the computational cost of training and testing increases with the number of tasks. To address this problem, heterogeneous learning is able to train a single classifier to perform multiple tasks. Heterogeneous learning is simultaneously train regression and recognition tasks, thereby reducing both training and testing time. However, it is difficult to obtain equivalent performance for set of single task classifiers due to variance of training error of each task. In this paper, we propose weighted heterogeneous learning of a convolutional neural network with a weighted error function. Our method outperformed the conventional method in terms of facial attribute recognition, especially for regression tasks such as facial point detection, age estimation, and smile ratio estimation.
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
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998). doi:10.1007/BFb0054760
Dantone, M., Gall, J., Fanelli, G., Gool, L.V.: Real-time facial feature detection using conditional regression forests. In: Computer Vision and Pattern Recognition (2012)
Lian, H.-C., Lu, B.-L.: Multi-view gender classification using local binary patterns and support vector machines. In: Wang, J., Yi, Z., Zurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3972, pp. 202–209. Springer, Heidelberg (2006). doi:10.1007/11760023_30
Guo, G., Fu, Y., Dyer, C.R., Huang, T.S.: Image-based human age estimation by manifold learning and locally adjusted robust regression. IEEE Trans. Image Process. 17, 1178–1188 (2008)
Krizhevsky, A., Ilva, S., Hinton, G.E.: ImageNet classification with deep convolutional neural network. In: Advances in Neural Information Processing System, vol. 25, pp. 1097–1105 (2012)
Sun, Y., Wang, X., Tang, X.: Deep convolutional network cascade for facial point detection. In: Computer Vision and Pattern Recognition (2013)
Zhou, E., Fan, H., Cao, Z., Jiang, Y., Yin, Q.: Extensive facial landmark localization with coarse-to-fine convolutional network cascade. In: IEEE International Conference on Computer Vision Workshops (2013)
Yamashita, T., Watasue, T., Yamauchi, Y., Fujiyoshi, H.: Facial point detection using convolutional neural network transferred from a heterogeneous task. In: International Conference on Image Processing (2015)
Kimura, M., Yamashita, T., Yamauchi, Y., Fujiyoshi, H.: Facial point detection based on a convolutional neural network with optimal mini-batch procedure. In: IEEE International Conference on Image Processing (2015)
Wu, Y., Ji, Q.: Discriminative deep face shape model for facial point detection. Int. J. Comput. Vis. 113, 37–53 (2015)
Jourabloo, A., Liu, X.: Large-pose face alignment via CNN-based dense 3D model fitting. In: Computer Vision and Pattern Recognition (2016)
Yan, C., Lang, C., Wang, T., Du, X., Zhang, C.: Age estimation based on convolutional neural network. In: Ooi, W.T., Snoek, C.G.M., Tan, H.K., Ho, C.-K., Huet, B., Ngo, C.-W. (eds.) PCM 2014. LNCS, vol. 8879, pp. 211–220. Springer, Heidelberg (2014). doi:10.1007/978-3-319-13168-9_22
Eidinger, E., Enbar, R., Hassner, T.: Age and gender estimation of unfiltered faces. In: IEEE Press Transactions on Information Forensics and Security (2014)
Zhu, Y., Yan, L., Guowang, M., Guodong, G.: A study on apparent age estimation. In: IEEE International Conference on Computer Vision Workshops (2015)
Kuang, Z., Huang, C., Zhang, W.: Deeply learned rich coding for cross-dataset facial age estimation. In: IEEE International Conference on Computer Vision Workshops, pp. 96–101 (2015)
Tivive, F.H.C., Bouzerdoum, A.: A gender recognition system using shunting inhibitory convolutional neural networks. In: Neural Networks, pp. 5336–5341 (2006)
Antipov, G., Berrani, S.A., Dugelay, J.-L.: Minimalistic CNN-based ensemble model for gender prediction from face images. Pattern Recogn. Lett. 70, 59–65 (2015)
Levi, G., Hassner, T.: Age and gender classification using convolutional neural networks. In: Computer Vision and Pattern Recognition (2015)
Argyriou, A., Evgeniou, T., Pontil, M.: Convex multi-task feature learning. Mach. Learn. 73(3), 243–272 (2008). Kluwer Academic Publishers
Zhang, Z., Luo, P., Loy, C.C., Tang, X.: Facial landmark detection by deep multi-task learning. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 94–108. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10599-4_7
Devries, T., Biswaranjan, K., Taylor, G.W.: Multi-task learning of facial landmarks and expression. In: Computer and Robot Vision (2014)
Zhang, Z., Luo, P., Loy, C.C., Tang, X.: Learning deep representation for face alignment with auxiliary attributes. arXiv preprint arXiv:1408.3967 (2015)
Yim, J., Jung, H., Yoo, B., Choi, C., Park, D., Kim, J.: Rotating your face using multi-task deep neural network. In: Computer Vision and Pattern Recognition (2015)
Ranjan, R., Vishal, M.P., Chellappa, R.: HyperFace: a deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. arXiv preprint arXiv:1603.01249 (2016)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. In: Neurocomputing, pp. 696–699 (1988)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Fukui, H. et al. (2017). Multiple Facial Attributes Estimation Based on Weighted Heterogeneous Learning. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10117. Springer, Cham. https://doi.org/10.1007/978-3-319-54427-4_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-54427-4_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-54426-7
Online ISBN: 978-3-319-54427-4
eBook Packages: Computer ScienceComputer Science (R0)