Unconstrained and NIR Face Detection with a Robust and Unified Architecture

  • Priyabrata Dash
  • Dakshina Ranjan KiskuEmail author
  • Jamuna Kanta Sing
  • Phalguni Gupta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10954)


This paper proposes a face detection method making use of Fast Successive Mean Quantization Transform (FSMQT) features for image representation to deal with illumination and sensor insensitive issues of the individual as well as the crowd face images. A split up Sparse Network of Winnows (SNoW) with Winnow updating rule is then exploited to speed up the original SNoW classifier. Features and classifiers are combined together with skin detection algorithm for fake face detection in crowd image and head orientation correction for near infrared faces. The experiment is performed with four databases, viz. BIOID, LFW, FDDB and IIT Delhi near infrared showing superior performance.


Face detection Fast SMQT Split up SNOW classifier Pose Occlusion Blur Labeled faces Crowd faces 


  1. 1.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of Computer Vision and Pattern Recognition (CVPR) (2001)Google Scholar
  2. 2.
    Li, H., Lin, Z., Brandt, J., Shen, X., Hua, G.: Efficient boosted exemplar-based face detection. In: Proceedings of Computer Vision and Pattern Recognition (CVPR) (2014)Google Scholar
  3. 3.
    Yan, J., Zhang, X., Lei, Z., Li, S.Z.: Real-time high performance deformable model for face detection in the wild. In: Proceedings of International Conference on Biometrics (ICB) (2013)Google Scholar
  4. 4.
    Liao, S., Jain, A.K., Li, S.Z.: A fast and accurate unconstrained face detector. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 211–223 (2016)CrossRefGoogle Scholar
  5. 5.
    Nilsson, M., Nordberg, J., Claesson, I.: Face detection using local SMQT features and split up SNOW classifier. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), no. 2, pp. 589– 592 (2007)Google Scholar
  6. 6.
    Yang, B., Yan, J., Lei, Z., Li, S.Z.: Aggregate channel features for multi-view face detection. In: Proceedings of IEEE International Joint Conference on Biometrics (IJCB), pp. 1–8 (2014)Google Scholar
  7. 7.
    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). Scholar
  8. 8.
    Yang, H., Wang, X.A.: Cascade classifier for face detection. J. Algorithms Comput. Technol. 10(3), 187–197 (2016)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Roth, D., Yang, M., Ahuja, N.: A snow-based face detector. In: Proceedings of Advances in Neural Information Processing Systems (NIPS), pp. 855–861 (2000)Google Scholar
  10. 10.
    Prathibha, E., Manjunath, A., Likitha, R.: RGB to YCbCr color conversion using VHDL approach. Int. J. Eng. Res. Dev. 1(3), 15–22 (2012)Google Scholar
  11. 11.
    Fröba, B., Ernst, A.: Face detection with the modified census transform. In: Proceedings of 6th IEEE International Conference on Automatic Face and Gesture Recognition (FG), pp. 91–96 (2004)Google Scholar
  12. 12.
  13. 13.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Bellhumer, P.N., Hespanha, J., Kriegman, D.: Eigen faces vs. fisher faces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. Spec. Issue Face Recogn. 17(7), 711–720 (1997)CrossRefGoogle Scholar
  15. 15.
    Samaria, F., Harter, A.: Parameterization of a stochastic model for human face identification. In: Proceedings of 2nd IEEE Workshop on Applications of Computer Vision (WACV) (1994)Google Scholar
  16. 16.
    Heisele, B., Poggio, T., Pontil, M.: Face detection in still gray images. Technical report, Center for Biological and Computational Learning, MIT, A.I. Memo 1687 (2000)Google Scholar
  17. 17.
    Sanderson, C., Lovell, B.C.: Multi-region probabilistic histograms for robust and scalable identity inference. In: Tistarelli, M., Nixon, M.S. (eds.) ICB 2009. LNCS, vol. 5558, pp. 199–208. Springer, Heidelberg (2009). Scholar
  18. 18.
  19. 19.
    Huang, B.G., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. University of Massachusetts, Amherst, Technical report 07-49 (2007)Google Scholar
  20. 20.
    Jain, V., Learned-Miller, E.: FDDB: a benchmark for face detection in unconstrained settings. Technical report, University of Massachusetts, Amherst (2010)Google Scholar
  21. 21.

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Priyabrata Dash
    • 1
  • Dakshina Ranjan Kisku
    • 1
    Email author
  • Jamuna Kanta Sing
    • 2
  • Phalguni Gupta
    • 3
  1. 1.Department of Computer Science and EngineeringNational Institute of Technology DurgapurDurgapurIndia
  2. 2.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia
  3. 3.National Institute of Technical Teachers Training and ResearchSalt Lake, KolkataIndia

Personalised recommendations