Skip to main content
SpringerLink
Log in

Unconstrained and NIR Face Detection with a Robust and Unified Architecture

  • Conference paper
  • First Online:
Intelligent Computing Theories and Application (ICIC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10954))

Included in the following conference series:

  • 2775 Accesses

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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. 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. 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. 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)

    Article  Google Scholar 

  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. 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. 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). https://doi.org/10.1007/978-3-319-10599-4_8

    Chapter  Google Scholar 

  8. Yang, H., Wang, X.A.: Cascade classifier for face detection. J. Algorithms Comput. Technol. 10(3), 187–197 (2016)

    Article  MathSciNet  Google Scholar 

  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. 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. 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. https://www.toptal.com/algorithms/successive-mean-quantization-transform

  13. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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). https://doi.org/10.1007/978-3-642-01793-3_21

    Chapter  Google Scholar 

  18. https://www.bioid.com/About/BioID-Face-Database

  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. Jain, V., Learned-Miller, E.: FDDB: a benchmark for face detection in unconstrained settings. Technical report, University of Massachusetts, Amherst (2010)

    Google Scholar 

  21. http://www.comp.polyu.edu.hk/~csajaykr/IITD/FaceIR.htm

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Durgapur, Durgapur, 713209, India

    Priyabrata Dash & Dakshina Ranjan Kisku

  2. Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India

    Jamuna Kanta Sing

  3. National Institute of Technical Teachers Training and Research, Salt Lake, Kolkata, 700106, India

    Phalguni Gupta

Authors
  1. Priyabrata Dash
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dakshina Ranjan Kisku
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jamuna Kanta Sing
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Phalguni Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dakshina Ranjan Kisku .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytechnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

  4. Indian Institute of Technology Kanpur, Kanpur, India

    Phalguni Gupta

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dash, P., Kisku, D.R., Sing, J.K., Gupta, P. (2018). Unconstrained and NIR Face Detection with a Robust and Unified Architecture. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10954. Springer, Cham. https://doi.org/10.1007/978-3-319-95930-6_88

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-95930-6_88

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95929-0

  • Online ISBN: 978-3-319-95930-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation