Face Occlusion Detection Using Cascaded Convolutional Neural Network

  • Yongliang Zhang
  • Yang Lu
  • Hongtao Wu
  • Conglin Wen
  • Congcong Ge
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9967)

Abstract

With the rise of crimes associated with ATM, face occlusion detection has gained more and more attention because it facilitates the surveillance system of ATM to enhance the safety by pinpointing disguised among customers and giving alarms when suspicious customer is found. Inspired by strong learning ability of deep learning from data and high efficient feature representation ability, this paper proposes a cascaded Convolutional Neural Network (CNN) based face occlusion detection method. In the proposed method, three cascaded CNNs are used to detect head, eye occlusion and mouth occlusion. Experimental results show that the proposed method is very effective on two test datasets.

Keywords

Cascaded convolutional neural network Face occlusion detection ATM 

References

  1. 1.
    Eum, S., Suhr, J.K., Kim, J.: Face recognition ability evaluation for ATM applications with exceptional occlusion handling. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 82–89 (2011)Google Scholar
  2. 2.
    Wen, C.Y., Chiu, S.H., Liaw, J.J., Lu, C.P.: The safety helmet detection for ATM’s surveillance system via the modified Hough transforms. In: Security Technology IEEE 37th Annual 2003 International Carnahan Conference, pp. 364–369 (2003)Google Scholar
  3. 3.
    Liu, C.-C., Liao, J.-S., Chen, W.-Y., Chen, J.-H.: The full motorcycle helmet detection scheme using canny detection. In: 18th IPPR Conference on Computer Vision, Graphics and Image Processing (CVGIP), pp. 1104–1110 (2005)Google Scholar
  4. 4.
    Wen, C., Chiu, S., Tseng, Y., Lu, C.: The mask detection technology for occluded face analysis in the surveillance system. J. Forensic Sci. 50(3), 1–9 (2005)CrossRefGoogle Scholar
  5. 5.
    Min, R., D’Angelo, A., Dugelay, J.-L.: Efficient scarf detection prior to face recognition. In: Proceedings of the 18th European Signal Processing Conference, August 2010, pp. 259–263 (2010)Google Scholar
  6. 6.
    Lin, D.-T., Liu, M.-J.: Face occlusion detection for automated teller machine surveillance. In: Chang, L.-W., Lie, W.-N. (eds.) PSIVT 2006. LNCS, vol. 4319, pp. 641–651. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Kim, G., Suhr, J.K., Jung, H.G., Kim, J.: Face occlusion detection by using B-spline active contour and skin color information. In: The 11th International Conference on Control Automation Robotics & Vision (ICARCV), pp. 627–632 (2010)Google Scholar
  8. 8.
    Zhang, X., Zhou, L., Zhang, T., Yang, J.: A novel efficient method for abnormal face detection in ATM. In: 2014 International Conference on Audio, Language and Image Processing (ICALIP), pp. 695–700 (2014)Google Scholar
  9. 9.
    Dong, W., Soh, Y.: Image-based fraud detection in automatic teller machine. Int. J. Comput. Sci. Netw. Secur. 6, 13–18 (2006)Google Scholar
  10. 10.
    Choi, I., Kim, D.: Facial fraud discrimination using detection and classification. In: 6th International Symposium Advances in Visual Computing, pp. 199–208 (2010)Google Scholar
  11. 11.
    Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes. In: Computer Vision and Pattern Recognition, pp. 1891–1898. IEEE (2014)Google Scholar
  12. 12.
    Martinez, A.M., Benavente, R.: The AR face database. CVC Technical report #24 (1998)Google Scholar
  13. 13.
    Chen, B.-C., Chen, C.-S., Hsu, W.H.: Cross-age reference coding for age-invariant face recognition and retrieval. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part VI. LNCS, vol. 8694, pp. 768–783. Springer, Heidelberg (2014)Google Scholar
  14. 14.
    Yi, D., Lei, Z., Liao, S., et al.: Learning face representation from scratch. Eprint Arxiv (2014)Google Scholar
  15. 15.
    Zhaohua, C.: Research of Occluded Face Detection and Recognition Based on Video. Soochow University, Suzhou (2012)Google Scholar
  16. 16.
    Gong, N.N,, Hai-Yan, W.U.: Projection curve gray level difference based face occlusion detection. Sci. Technol. Eng. (2013) Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Yongliang Zhang
    • 1
  • Yang Lu
    • 1
  • Hongtao Wu
    • 2
  • Conglin Wen
    • 1
  • Congcong Ge
    • 1
  1. 1.College of Computer Science and TechnologyZhejiang University of TechnologyHangzhouChina
  2. 2.School of Computer Science and EngineeringHebei University of TechnologyTianjinChina

Personalised recommendations