Skip to main content
SpringerLink
Log in

A Systematic Training Procedure for Viola-Jones Face Detector in Heterogeneous Computing Architecture

  • Published:
Journal of Grid Computing Aims and scope Submit manuscript

Abstract

The face detection has become one of the most important topics in machine learning and computer vision in the last few decades. Many papers have been published utilizing various methods for face detection. One of the most popular face detectors used in many practical applications with heterogeneous computing architecture is Viola-Jones method. Despite of being a real-time and robust face detector, it suffers from not well explained parts at the training procedure, e.g., not clear how to select few features in the first cascades or not clear how many samples are needed and how to gather a good trainset for training a cascade. In this paper, a trainset selection method based on histograms generated from AdaBoost and in addition, simple ways to select few features in beginning cascades are proposed. The training procedure is then compared to a baseline training presented in the previous studies.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57, 137–154 (2004)

    Article  Google Scholar 

  2. Wang, Y.Q.: An analysis of the Viola-Jones face detection algorithm. Image Processing On Line IPOL. 4, 128–148 (2014)

    Article  Google Scholar 

  3. D. Peleshko, K. Soroka, “Research of usage of Haar-like features and AdaBoost algorithm in Viola Jones method of object detection”, 12th International Conference on the Experience of Designing and Application of CAD Systems in Microelectronics (CADSM), pp. 284-286, 2013

  4. S. R. Lang, M. H. Luerssen, D. M. W. Powers, “Evolutionary Feature Preselection for Viola-Jones Classifier Training”, Congress on Engineering and Technology (S-CET), pp. 1–4, 2012

  5. C. Lo, P. Chow, “A high-performance architecture for training Viola-Jones object detectors”, Field-Programmable Technology (FPT), International Conference on, pp. 174–181, 2012

  6. J. Masek, R. Burget, V. Uher, S.Güney, “Speeding up Viola-Jones algorithm using multi-Core GPU implementation”, Telecommunications and Signal Processing (TSP), 36th International Conference on, pp. 808–812, 2013

  7. Q. Li, U. Niaz, B. Merialdo, “An improved algorithm on Viola Jones object detector”, Content-Based Multimedia Indexing (CBMI), 10th International Workshop on, pp. 1–6, 2012

  8. Acasandrei, L., Barriga, A.: AMBA bus hardware accelerator IP for Viola-Jones face detection. IET Comput. Digit. Tech. 7, 200–209 (2013)

    Article  Google Scholar 

  9. Kawulok, M., Szymanek, J.: Precise multi-level face detector for advanced analysis of facial images. IET Image Process. 6, 95–103 (2012)

    Article  MathSciNet  Google Scholar 

  10. Stojmenovic, M.: Pre-Eliminating Features for Fast Training in Real Time Object Detection in Images with a Novel Variant of AdaBoost. IEEE Conference on Cybernetics and Intelligent Systems (2006)

  11. Wang, W., Zhang, Y., Yan, S., Zhang, Y., Jia, H.: Parallelization and performance optimization on face detection algorithm with OpenCL: a case study. Tsinghua Sci. Technol. 17(3), 287–295 (2012)

    Article  Google Scholar 

  12. Luh, G.C.: Face detection using combination of skin color pixel detection and Viola-Jones face detector. International Conference on Machine Learning and Cybernetics. 364–370 (2014)

  13. S. K. Paul, M. S. Uddin, S. Bouakaz, “Extraction of facial feature points using cumulative distribution function by varying single threshold group”, Informatics, Electronics & Vision (ICIEV), International Conference on, pp.806–811, 2012

  14. Shen, C., Paisitkriangkrai, S., Zhang, J.: Efficiently learning a detection Cascade with sparse eigenvectors. IEEE Trans. Image Process. 20, 22–35 (2010)

    Article  MathSciNet  Google Scholar 

  15. Mita, T., Kaneko, T., Stenger, B., Hori, O.: Discriminative feature co-occurrence selection for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 30, 1257–1269 (2008)

    Article  Google Scholar 

  16. N. Ai-Min, “Fast Face Detection Based on Wavelet Transform in the Color Image”, Intelligent Systems Design and Engineering Applications (ISDEA), Fifth International Conference on,pp. 474–477, 2014

  17. Guo-yun, Z., Long-yuan, G., Jian-hui, W., Hong-min, L., Guan-qi, G.: “Face detection using Gabor wavelet and SVM”, International Conference on Computer Application and System Modeling (ICCASM 2010). Vol. 2, 588–590 (2010)

  18. W. Chuan-xu, L. Xue, “Face Detection using BP Network Combined with Gabor Wavelet Transform”, Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, SNPD '08. Ninth ACIS International Conference on, pp. 526–529, 2008

  19. Jiuxian, L., Siyu, X., Liangzheng, X.: Face detection based on self-skin segmentation and wavelet support vector machine. International Conference on Computational Intelligence and Security. 1, 755–758 (2006)

    Google Scholar 

  20. Tofighi, A., Monadjemi, S.A.: “Face detection and recognition using skin color and AdaBoost algorithm combined with Gabor features and SVM classifier”, Multimedia and Signal Processing (CMSP), International Conference on. 1, 141–145 (2011)

  21. Shavers, C., Li, R., Lebby, G.: An SVM-based approach to face detection. Proceeding of the Thirty-Eighth Southeastern Symposium on System Theory. 362–366 (2006)

  22. Wang, Y., Yuan, B.: A novel approach for human face detection from color images under complex background. Pattern Recogn. 34, 1983–1992 (2001)

    Article  Google Scholar 

  23. G. Gomez, M. Sanchez, L. Sucar, “On selecting an appropriate colour space for skin detection”, Mexican International Conference on Artificial Intelligence (MICAI), pp. 69–78, 2002

  24. D. Chai, K.N. Ngan, “Face segmentation using skin-color map in videophone applications”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 9, pp. 551–564,1999.

  25. Tavallali, P., Yazdi, M.: “Robust Skin Detector Based on AdaBoost and Statistical Luminance Features”, ICTCK'15. Mashhad, Iran (2015)

    Google Scholar 

  26. Bhatia, A.R., Patel, N.M., Chauhan, N.C.: “Parallel Implementation of Face Detection Algorithm on GPU”, 2nd International Conference on Next Generation Computing Technologies (2016)

    Google Scholar 

  27. Lee, Y., Jang, C., Kim, H.: Accelerating a Computer Vision Algorithm on a Mobile SoC Using CPU-GPU Co-Processing - a Case Study on Face Detection. International Conference on Mobile Software Engineering and Systems (2016)

  28. Tavallali, P., Yazdi, M., Khosravi, M.R.: “An Efficient Training Procedure for Viola-Jones Face Detector”, CSCI'17. Las Vegas, USA (Dec. 2017)

    Google Scholar 

  29. Tavallali, P., Yazdi, M., Khosravi, M.R.: Robust cascaded skin detector based on AdaBoost. Multimed. Tools Appl. 78(2), 2599–2620 (2019)

    Article  Google Scholar 

  30. Khan, F.A.: M. Ur Rehman, A. Khalid, M. Ali, M. Imran, M. Nawaz, et al., “an intelligent data service framework for heterogeneous data sources”. Journal of Grid Computing. 17, 577–589 (2019)

    Article  Google Scholar 

  31. Caballer, M., Zala, S., García, Á.L., Moltó, G., Fernández, P.O., Velten, M.: Orchestrating complex application architectures in heterogeneous clouds. Journal of Grid Computing. 16, 3–18 (2018)

    Article  Google Scholar 

  32. Jiang, H., Learned-Miller, E.: “Face Detection with the Faster R-CNN”, 12th IEEE International Conference on Automatic Face & Gesture RecognitionUSA (2017). https://doi.org/10.1109/FG.2017.82

    Book  Google Scholar 

  33. Li, Y., Sun, B., Wu, T., Wang, Y.: Face detection with end-to-end integration of a ConvNet and a 3D model. In: Face Detection with End-to-End Integration of a ConvNet and a 3D Model. European Conference on Computer Vision (2016). https://doi.org/10.1007/978-3-319-46487-9_26

  34. H. Qin, J. Yan, X. Li, X. Hu, “Joint training of cascaded CNN for face detection”, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3456–3465, USA, 2016

  35. M. Nauman, H. Ur Rehman, G. Politano, and A. Benso, “Beyond Homology Transfer: Deep Learning for Automated Annotation of Proteins”, Journal of Grid Computing, vol. 17, pp. 225–237, 2019/06/01 2019

  36. Suna, X., Wua, P., Hoi, S.: Face detection using deep learning: an improved faster RCNN approach. Neurocomputing. 299, 42–50 (2018)

    Article  Google Scholar 

  37. J. Ruiz-del-Solar, and R. Verschae “Skin Detection using Neighborhood Information”, Proceedings of the 6th International Conference on Automatic Face and Gesture Recognition , pp. 463–468, 2004

  38. Casati, J., Moraes, D., Rodrigues, E.: SFA: a Human Skin Image Database Based on FERET and AR Facial Images. IX Workshop de Visao Computacional, Anais do VIII Workshop de VisaoComputacional (2013)

    Google Scholar 

  39. Kovac, J., Peer, P., Solina, F.: Human Skin Color Clustering for Face Detection. EUROCON (2003)

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, University of California, Merced, USA

    Pooya Tavallali

  2. Department of Communications and Electronic Engineering, Shiraz University, Shiraz, Iran

    Mehran Yazdi

  3. Department of Electrical and Electronic Engineering, Shiraz University of Technology, Shiraz, Iran

    Mohammad R. Khosravi

Authors
  1. Pooya Tavallali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mehran Yazdi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad R. Khosravi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pooya Tavallali.

Ethics declarations

Conflict of Interest

There is no conflict of interests.

Data Availability

It is available through the corresponding author.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tavallali, P., Yazdi, M. & Khosravi, M.R. A Systematic Training Procedure for Viola-Jones Face Detector in Heterogeneous Computing Architecture. J Grid Computing 18, 847–862 (2020). https://doi.org/10.1007/s10723-020-09517-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10723-020-09517-z

Keywords

Search

Navigation