Skip to main content

A Face Detection Using Support Vector Machine: Challenging Issues, Recent Trend, Solutions and Proposed Framework

  • Conference paper
  • First Online:
Advances in Computing and Data Sciences (ICACDS 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1046))

Included in the following conference series:

Abstract

Face detection comes under the domain of object detection and tracking. Face detection is an integral part of the motion based object detection which combines digital image processing and computer vision for the detection of instances and faces as well. This paper provides a brief overview of the recent trends; current open challenging issues and their solutions available for efficient detection of faces form video stream or still images. This paper also discusses various approaches which are widely used to detect the faces in the dynamic background, illumination and other current challenges. In the last section, a framework for face detection is also proposed using SVM classifier.

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

Access this chapter

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

Similar content being viewed by others

References

  1. Wang, M., Wang, Z., Li, J.: Deep convolutional neural network applies to face recognition in small and medium databases. In: 2017 4th International Conference on Systems and Informatics (ICSAI), pp. 1368–1372 (2018)

    Google Scholar 

  2. Triantafyllidou, D., Tefas, A.: Face detection based on deep convolutional neural networks exploiting incremental facial part learning. In: 23rd International Conference on Pattern Recognition (ICPR), pp. 3560–3565 (2017)

    Google Scholar 

  3. Er, M.J., Wu, S., Lu, J., Toh, H.L.: Face recognition with radial basis function (RBF) neural networks. IEEE Trans. Neural Netw. 13, 697–710 (2002)

    Article  Google Scholar 

  4. Aziz, K.A.A., Ramlee, R.A., Abdullah, S.S., Jahari, A.N.: Face detection using radial basis function neural networks with variance spread value. In: 2009 International Conference of Soft Computing and Pattern Recognition, pp. 399–403 (2009)

    Google Scholar 

  5. Yoo, S.H., Oh, S.K., Pedrycz, W.: Optimized face recognition algorithm using radial basis function neural networks and its practical applications. Neural Netw. 69, 111–125 (2015)

    Article  Google Scholar 

  6. Kim, K., et al.: Face recognition using support vector machines with local correlation kernels. Int. J. Pattern Recogn. Artif. Intell. 16, 97–111 (2002)

    Article  Google Scholar 

  7. Zhou, S.K., Chellappa, R.: Multiple-exemplar discriminant analysis for face recognition. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 4, pp. 191–194 (2004)

    Google Scholar 

  8. Ohlyam, S., Sangwan, S., Ahuja, T.: A survey on various problem & challenges in face recognition. Int. J. Eng. Res. Technol. 2(6), 2533–2538 (2013)

    Google Scholar 

  9. Tsai, C.C., et al.: Face detection using eigenface and neural network. In: 2006 IEEE International Conference on Systems, Man and Cybernetics, pp. 4343–4347 (2007)

    Google Scholar 

  10. Sharifara, A., et al.: A general review of human face detection including a study of neural networks and haar feature–based cascade classifier in face detection. In: 2014 International Symposium on Biometric and Security Technologies (ISBAST 2015) (2015)

    Google Scholar 

  11. Tayyab, M., Zafar, M.F.: Face detection using 2D-discrete cosine transform and back propagation neural network. In: 2009 International Conference of Emerging Technologies (2009)

    Google Scholar 

  12. El-Bakry, H.M.: Face detection using neural networks and image decomposition. In: Proceedings of International Joint Conference on Neural Networks, IJCNN 2002 (2002)

    Google Scholar 

  13. Jamil, N., Iqbal, S., Iqbal, N.: Face recognition using Neural Networks. In: Proceedings of IEEE International Multi Topic Conference, Technology for the 21st Century, pp. 416–419 (2001)

    Google Scholar 

  14. Huang, D.Y., Chen, C.H., Chen, T.Y.: Real-time face detection using a moving camera. In: 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (2018)

    Google Scholar 

  15. Dang, K., Sharma, S.: Review and comparison on face detection algorithms. In: 2017 7th International Conference on Cloud Computing, Data Science & Engineering – Conference (2017)

    Google Scholar 

  16. Fernandez, M.C.D., et al.: Simultaneous face detection and recognition using Viola-Jones algorithm and artificial neural networks for identity verification. In: 2014 IEEE Region 10 Symposium (2014)

    Google Scholar 

  17. Hilado, S.D.F., Dadios, E.P.: Face detection using neural networks with skin segmentation. In: 2011 IEEE 5th International Conference on Cybernetics and Intelligent Systems (CIS), pp. 261–265 (2011)

    Google Scholar 

  18. Lang, L., Gu, W.: Study on face detection algorithm for real-time face detection system. In: 2009 Second International Symposium on Electronic Commerce and Security (2009)

    Google Scholar 

  19. Face Detection Technologies. https://disruptionhub.com/5-applications-facial-recognition-technology/. Accessed 15 Oct 2018

  20. Sharma, L., Lohan, N.: Performance analysis of moving object detection using BGS techniques in visual surveillance. Int. J. Spatio-Temporal Data Sci. Indersci. 1(1), 22–53 (2019)

    Article  Google Scholar 

  21. Sharma, L., Yadav, D.K.: Histogram based adaptive learning rate for background modelling and moving object detection in video surveillance. Int. J. Telemed. Clin. Pract. Indersci. 2(1), 74–92 (2017)

    Google Scholar 

  22. Sharma, L., Lohan, N., Yadav, D.K.: A study of challenging issues on video surveillance system for object detection. J. Basic Appl. Eng. Res. 4(4), 313–318 (2017)

    Google Scholar 

  23. Sharma, L., Singh, S., Yadav, D.K.: Fisher’s linear discriminant ratio based threshold for moving human detection in thermal video. Infrared Phys. Technol. 78, 118–128 (2016)

    Article  Google Scholar 

  24. Sharma, L., Yadav, D.K., Bharti, S.: An improved method for visual surveillance using background subtraction technique. In: IEEE 2nd International Conference on Signal Processing and Integrated Networks (SPIN 2015), pp. 421–426. Amity University Noida, India (2015)

    Google Scholar 

  25. Yadav, D.K., Sharma, L., Bharti, S.: Fuzzy-rule based threshold for moving human detection in video. In: International Conference on Advanced and Agile Manufacturing (ICAM 2015) (2015)

    Google Scholar 

  26. Yadav, D.K., Sharma, L., Bharti, S.: Moving object detection in real-time visual surveillance using background subtraction technique. In: IEEE 14th International Conference in Hybrid Intelligent Computing (HIS 2014), pp. 79–84. Gulf University for Science and Technology, Kuwait (2014)

    Google Scholar 

  27. Min, R., Kose, N., Dugelay, J.L.: KinectFaceDB: a kinectdatabase for face recognition. IEEE Trans. Syst. Man Cybern.: Syst. 44(11), 1534–1548 (2014)

    Article  Google Scholar 

  28. Zohra, F.T., et al.: Occlusion detection and localization from kinect depth images. In: 2016 International Conference on Cyberworlds, pp. 189–196 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lavanya Sharma .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Makkar, S., Sharma, L. (2019). A Face Detection Using Support Vector Machine: Challenging Issues, Recent Trend, Solutions and Proposed Framework. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Kashyap, R. (eds) Advances in Computing and Data Sciences. ICACDS 2019. Communications in Computer and Information Science, vol 1046. Springer, Singapore. https://doi.org/10.1007/978-981-13-9942-8_1

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-9942-8_1

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9941-1

  • Online ISBN: 978-981-13-9942-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics