Abstract
As the first essential step of automatic face analysis, face detection always receives high attention. The performance of current state-of-the-art face detectors cannot fulfill the requirements in real-world scenarios especially in the presence of severe occlusions. This paper proposes a novel and effective approach to occlusion-robust face detection. It combines two major phases, i.e. proposal generation and classification. In the former, we combine both the proposals given by a coarse-to-fine shallow pipeline and a Region Proposal Network (RPN) based deep one respectively, to generate a more comprehensive set of candidate regions. In the latter, we further decide whether the regions are faces using a well-trained Faster R-CNN. Experiments are conducted on the WIDER FACE benchmark, and the results clearly prove the competency of the proposed method at detecting occluded faces.
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
Chen, D., Ren, S., Wei, Y., Cao, X., Sun, J.: Joint cascade face detection and alignment. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part VI. LNCS, vol. 8694, pp. 109–122. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10599-4_8
Ekenel, H.K., Stiefelhagen, R.: Why is facial occlusion a challenging problem? In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 299–308. Springer, Heidelberg (2009). doi:10.1007/978-3-642-01793-3_31
Li, H., Lin, Z., Shen, X., Brandt, J., Hua, G.: A convolutional neural network cascade for face detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5325–5334 (2015)
Lienhart, R., Maydt, J.: An extended set of haar-like features for rapid object detection. In: Proceedings of the 2002 International Conference on Image Processing, vol. 1, pp. I-900–I-903. IEEE (2002)
Mathias, M., Benenson, R., Pedersoli, M., Van Gool, L.: Face detection without bells and whistles. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part IV. LNCS, vol. 8692, pp. 720–735. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10593-2_47
Moghaddam, B., Pentland, A.: Probabilistic visual learning for object representation. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 696–710 (1997)
Osuna, E., Freund, R., Girosi, F.: Training support vector machines: an application to face detection. In: Proceedings of the 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 130–136. IEEE (1997)
Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Romdhani, S., Torr, P., Schölkopf, B., Blake, A.: Computationally efficient face detection. In: Proceedings of the Eighth IEEE International Conference on Computer Vision, ICCV 2001, vol. 2, pp. 695–700. IEEE (2001)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint: arXiv:1409.1556
Sung, K.K., Poggio, T.: Example-based learning for view-based human face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20(1), 39–51 (1998)
Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)
Yang, B., Yan, J., Lei, Z., Li, S.Z.: Aggregate channel features for multi-view face detection. In: 2014 IEEE International Joint Conference on Biometrics (IJCB), pp. 1–8. IEEE (2014)
Yang, M.H., Abuja, N., Kriegman, D.: Face detection using mixtures of linear subspaces. In: Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 70–76. IEEE (2000)
Yang, S., Luo, P., Loy, C.C., Tang, X.: From facial parts responses to face detection: a deep learning approach. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3676–3684 (2015)
Yang, S., Luo, P., Loy, C.C., Tang, X.: Wider face: a face detection benchmark (2016)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part I. LNCS, vol. 8689, pp. 818–833. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10590-1_53
Acknowledgments
This work was supported in part by the national key research and development plan under Grant 2016YFC0801002, the Hong Kong, Macao, and Taiwan Science and Technology Cooperation Program of China under Grant L2015TGA9004, and the National Natural Science Foundation of China under Grant 61540048 and 61673033.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Guo, J., Xu, J., Liu, S., Huang, D., Wang, Y. (2016). Occlusion-Robust Face Detection Using Shallow and Deep Proposal Based Faster R-CNN. In: You, Z., et al. Biometric Recognition. CCBR 2016. Lecture Notes in Computer Science(), vol 9967. Springer, Cham. https://doi.org/10.1007/978-3-319-46654-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-46654-5_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46653-8
Online ISBN: 978-3-319-46654-5
eBook Packages: Computer ScienceComputer Science (R0)