Abstract
The main precondition for applications such as face recognition and face de-identification for privacy protection is efficient face detection in real scenes. In this paper, we propose a hybrid cascade model for face detection in the wild. The cascaded two-stage model is based on the fast normalized pixel difference (NPD) detector at the first stage, and a deep convolutional neural network (CNN) at the second stage. The outputs of the NPD detector are characterized by a very small number of false negative (FN) and a much higher number of false positive face (FP) detections. The FP detections are typically an order of magnitude higher than the FN ones. This very high number of FPs has a negative impact on recognition and/or de-identification processing time and on the naturalness of the de-identified images. To reduce the large number of FP face detections, a CNN is used at the second stage. The CNN is applied only on vague face region candidates obtained by the NPD detector that have an NPD score in the interval between two experimentally determined thresholds. The experimental results on the Annotated Faces in the Wild (AFW) test set and the Face Detection Dataset and Benchmark (FDDB) show that the hybrid cascade model significantly reduces the number of FP detections while the number of FN detections are only slightly increased.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ribarić, S., Ariyaeeinia, A., Pavešić, N.: De-identification for privacy protection in multimedia content: A survey. Sig. Process. Image Commun. 47, 131–151 (2016)
Zhu, X., Ramanan, D.: Face detection, pose estimation, and landmark localization in the wild. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2879–2886 (2012)
Dollár, P., Tu, Z., Perona, P., Belongie, S.: Integral channel features. In: Proceedings of British Machine Vision Conference, pp. 1–11 (2009)
Liao, S., Jain, A.K., Li, S.Z.: A fast and accurate unconstrained face detector. IEEE TPAMI 38(2), 211–223 (2016)
Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: DeepFace: closing the gap to human-level performance in face verification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1701–1708 (2014)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of International Conference on Learning Representations (2014). http://arxiv.org/abs/1409.1556
Romdhani, S., Torr, P., Schölkopf, B., Blake, A.: Efficient face detection by a cascaded support–vector machine expansion. Proc. Roy. Soc. London A Math. Phys. Eng. Sci. 460(2051), 3283–3297 (2004)
Ranjan, R., Patel, V.M., Chellappa, R.: A deep pyramid deformable part model for face detection. In: IEEE 7th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1–8 (2015)
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. LNCS, vol. 8694, pp. 109–122. Springer, Cham (2014). doi:10.1007/978-3-319-10599-4_8
Dollár, P., Welinder P., Perona, P.: Cascaded pose regression. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1078–1085 (2010)
Ronghang, H., Ruiping, W., Shiguang, S., Xilin, C.: Robust head-shoulder detection using a two-stage cascade framework. In: 22nd International Conference on Pattern Recognition (ICPR), pp. 2796–2801 (2014)
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)
Marčetić, D., Hrkać, T., Ribarić, S.: Two-stage cascade model for unconstrained face detection. In: IEEE International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE), pp. 1–4 (2016)
The Annotated Faces in the Wild (AFW) testset. https://www.ics.uci.edu/~xzhu/face/. Accessed 21 Mar 2017
Jain, V., Learned-Miller, E.: FDDB: a benchmark for face detection in unconstrained settings. In: Technical report UM-CS-2010-009, Dept. of Computer Science, University of Massachusetts, Amherst (2010)
King, D.E.: Max-margin object detection. In: arXiv preprint arXiv:1502.00046 (2015)
Weber, E.H.: Tastsinn und Gemeingefühl. In: Wagner, R. (ed.) Hand-wörterbuch der Physiologie, vol. III, pp. 481–588. Vieweg, Braunschweig (1846)
Wu, Z., Huang, Y., Wang, L., Wang, X., Tan, T.: A comprehensive study on cross-view gait based human identification with deep cnns. IEEE TPAMI 39(2), 209–226 (2017)
King, D.E.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10, 1755–1758 (2009)
http://dlib.net/files/data/dlib_face_detection_dataset-2016-09-30.tar.gz. Accessed 21 Mar 2017
Mathias, M., Benenson, R., Pedersoli, M., Gool, L.: Face detection without bells and whistles. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 720–735. Springer, Cham (2014). doi:10.1007/978-3-319-10593-2_47
Acknowledgments
This work has been supported by the Croatian Science Foundation under project 6733 De-identification for Privacy Protection in Surveillance Systems (DePPSS).
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
Marčetić, D., Soldić, M., Ribarić, S. (2017). Hybrid Cascade Model for Face Detection in the Wild Based on Normalized Pixel Difference and a Deep Convolutional Neural Network. In: Felsberg, M., Heyden, A., Krüger, N. (eds) Computer Analysis of Images and Patterns. CAIP 2017. Lecture Notes in Computer Science(), vol 10425. Springer, Cham. https://doi.org/10.1007/978-3-319-64698-5_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-64698-5_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-64697-8
Online ISBN: 978-3-319-64698-5
eBook Packages: Computer ScienceComputer Science (R0)