Skip to main content
SpringerLink
Log in

Detection of Human Faces Using Neural Networks

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9948))

Included in the following conference series:

  • 2812 Accesses

Abstract

Human face detection is a key technology in machine vision applications including human recognition, access control, security surveillance and so on. This research proposes a precise scheme for human face detection using a hybrid neural network. The system is based on visual information of the face image sequences and is commenced with estimation of the skin area depending on color components. In this paper we have considered HSV and YCbCr color space to extract the visual features. These features are used to train the hybrid network consisting of a bidirectional associative memory (BAM) and a back propagation neural network (BPNN). The BAM is used for dimensional reduction and the multi-layer BPNN is used for training the facial color features. Our system provides superior performance comparable to the existing methods in terms of both accuracy and computational efficiency. The low computation time required for face detection makes it suitable to be employed in real time applications.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Darrell, T., Tollmar, K., Bentley, F., Checka, N., Morency, L.-P., Rahimi, A., Oh, A.: Face-responsive interfaces: from direct manipulation to perceptive presence. In: International Conference of Ubiquitous Computing (2002)

    Google Scholar 

  2. Li, H., Lin, Z., Shen, X., Brandt, J., Hua, G.: A convolutional neural network cascade for face detection. In: CVPR 2015, pp. 5325–5334

    Google Scholar 

  3. Yang, M.-H., Kriegman, D.J., Ahuja, N.: Detecting faces in images: a survey. IEEE Trans. PAMI 24(1), 34–58 (2002)

    Article  Google Scholar 

  4. Shinn-Ying, H., Hui-Ling, H.: Facial modeling from an uncalibrated face image using a coarse-to-fine genetic algorithm. Pattern Recogn. 34(8), 1015–1031 (2001)

    MATH  Google Scholar 

  5. Goudail, F., Francois, E., Lange, T., Iwamoto, K.Kazuo, Otsu, N.: Face recognition system using local autocorrelations and multiscale integration. IEEE Trans. Pattern Anal. Mach. Intell. 18(10), 1024–1028 (1996)

    Article  Google Scholar 

  6. Rowley, H., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20(1), 23–37 (1998)

    Article  Google Scholar 

  7. Heisele, B., Serre, T., Poggio, T.: A component-based framework for face detection and identification. Int. J. Comput. Vis. 74(2), 167–181 (2007)

    Article  Google Scholar 

  8. Uddin, J., Mondal, A.M., Hoque Chowdhury, M.M., Bhuiyan, M.A.-A.: Face detection using genetic algorithm. In: Proceedings of 6th International Conference on Computer and Information Technology, Dhaka, Bangladesh, pp. 41–46, December 2003

    Google Scholar 

  9. Talele, K.T., Kadam, S., Tikare, A.: Efficient face detection using adaboost. In: IJCA Proceedings of International Conference in Computational Intelligence (2012)

    Google Scholar 

  10. Kukenys, I., McCane, B.: Support vector machines for human face detection. In: Proceedings of New Zealand Computer Science Research Student Conference (2008)

    Google Scholar 

  11. Sung, K., Pogio, T.: Example-based learning for view-based human face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20(1), 39–50 (1998)

    Article  Google Scholar 

  12. Lam, K., Hong, Y.: Locating and extracting the eye in human face images. Pattern Recogn. 29(5), 771–779 (1996)

    Article  Google Scholar 

  13. Moghaddam, B., Pentland, A.: Face recognition using view-based modular eigenspaces. In: Proceedings of Automatic Systems for the Identification and Inspection of Humans, SPIE, vol. 2277, July 1994

    Google Scholar 

  14. Shen, X., Lin, Z., Brandt, J., Wu, Y.: Detecting and aligning faces by image retrieval. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2013)

    Google Scholar 

  15. Li, H., Lin, Z., Shen, X., Brandt, J., Hua, G.: A convolutional neural network cascade for face detection. In: IEEE Conference on Computer Vision and Pattern Recognition (2015)

    Google Scholar 

  16. Chowdhury, M., Gao, M., Islam, R.: Fuzzy logic based filtering for image de-noising. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2016), Vancouver, Canada, pp. 24–29, July 2016

    Google Scholar 

  17. Cheng, H.D., Shi, X.J.: A simple and effective histogram equalization approach to image enhancement. Digit. Signal Process. 14, 158–170 (2004)

    Article  Google Scholar 

  18. Hjelmas, E., Low, B.K.: Face detection: a survey. Comput. Vis. Image Underst. 83, 236–274 (2001)

    Article  MATH  Google Scholar 

  19. Extended Yale Face Database B. http://vision.ucsd.edu/~iskwak/ExtYaleDatabase/ExtYaleB.html

  20. http://cswww.essex.ac.uk/mv/allfaces/faces95.html

Download references

Author information

Authors and Affiliations

  1. School of Computing and Mathematics, Charles Sturt University, Bathurst, Australia

    Mozammel Chowdhury & Rafiqul Islam

  2. Discipline of Business Analytics, The University of Sydney Business School, Sydney, Australia

    Junbin Gao

Authors
  1. Mozammel Chowdhury
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Junbin Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rafiqul Islam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mozammel Chowdhury .

Editor information

Editors and Affiliations

  1. The University of Tokyo, Tokyo, Japan

    Akira Hirose

  2. Kobe University, Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology, Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences, Beijing, China

    Derong Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Chowdhury, M., Gao, J., Islam, R. (2016). Detection of Human Faces Using Neural Networks. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_77

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46672-9_77

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46671-2

  • Online ISBN: 978-3-319-46672-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation