Encyclopedia of Computer Graphics and Games

Living Edition
| Editors: Newton Lee

Digital Images Using Heuristic AdaBoost Haar Cascade Classifier Model, Detection of Partially Occluded Faces

Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-08234-9_371-1



Detection of partially occluded faces in digital images using AdaBoost Haar cascade classifier is a viable technique of face detection if the cascade training procedure is modified.


Face detection is one of the more popular applications of object detection in computer vision. The computer uses a series of mathematical algorithms, pattern recognition, and image processing to identify faces from an image or video input. Over the years, the technology of detecting faces has evolved proportional to its usage in various applications. The most known algorithm for face detection was introduced by Viola and Jones in 2001. They proposed a framework that produces real-time face detection by the means of a novel image representation known as integral image and incorporated the Haar basis functions that was used in the general framework of object detection (Papageorgiou et...

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


  1. Cerf, M., Harel, J., Einhäuser, W., Koch, C.: Predicting human gaze using low-level saliency combined with face detection. Adv. Neural Inf. Proces. Syst. 20, 241–248 (2008)Google Scholar
  2. Cootes, T., Baldock, E.R., Graham, J.: An introduction to active shape models. In: Image Processing and Analysis, pp. 223–248. Oxford University Press (2000)Google Scholar
  3. Cristinacce, D., Cootes, T.: Facial feature detection using AdaBoost with shape constraints. In: Proceedings of the British Machine Vision Conference 2003 (2003)Google Scholar
  4. Da'san, M., Alqudah, A., Debeir, O.: Face detection using Viola and Jones method and neural networks. In: 2015 International Conference on Information and Communication Technology Research (ICTRC), pp. 40–43 (2015)Google Scholar
  5. Devrari, K., Kumar, K.: Fast face detection using graphics processor. Int. J. Comput. Sci. Inf. Technol. 2, 1082–1086 (2011)Google Scholar
  6. El Kaddouhi, S., Saaidi, A., Abarkan, M.: Eye detection based on the Viola-Jones method and corners points. Multimed. Tools Appl. 76, 23077–23097 (2017)CrossRefGoogle Scholar
  7. Hjelmås, E., Low, B.: Face detection: a survey. Comput. Vis. Image Underst. 83, 236–274 (2001)CrossRefGoogle Scholar
  8. Huang, C., Al, H., Wu, B., Lao, S.: Boosting nested cascade detector for multi-view face detection. In: Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004, Vol. 2, pp. 415–418 (2004)Google Scholar
  9. Huang, C., Ai, H., Li, Y., Lao, S.: Vector boosting for rotation invariant multi-view face detection. In: Tenth IEEE International Conference on Computer Vision (ICCV’05), Vol. 1, pp. 446–453 (2005)Google Scholar
  10. Krpec, J., Němec, M.: Face detection CUDA accelerating. In: ACHI 2012, The Fifth International Conference on Advances in Computer-Human Interactions, pp. 155–160 (2012)Google Scholar
  11. Lai, H., Savvides, M., Chen, T.: Proposed FPGA hardware architecture for high frame rate (≫100 fps) face detection using feature cascade classifiers. In: 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems (2007)Google Scholar
  12. Lienhart, R., Maydt, J.: An extended set of Haar-like features for rapid object detection. In: Proceedings. International Conference on Image Processing (2002)Google Scholar
  13. Mahalingam, G., Ricanek, K., Albert, A.: Investigating the Periocular-based face recognition across gender transformation. IEEE Trans. Inf. For. Secur. 9, 2180–2192 (2014)CrossRefGoogle Scholar
  14. Mahmoud, T., Abdel-latef, B., Abd-El-Hafeez, T., Omar, A.: An effective hybrid method for face detection. In: Proceedings of the Fifth International Conference on Intelligent Computing and Information Systems, Cairo, pp. 263–268 (2011)Google Scholar
  15. Mita, T., Kaneko, T., Hori, O.: Joint Haar-like features for face detection. In: Tenth IEEE International Conference on Computer Vision (ICCV’05), Vol. 1, pp. 1619–1626 (2005)Google Scholar
  16. Modi, M., Macwan, F.: Face detection approaches: a survey. Int. J. Innov. Res. Sci. Eng. Technol. 3, 11107–11116 (2014)Google Scholar
  17. Papageorgiou, C., Oren, M., Poggio, T.: A general framework for object detection. In: Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), Vol. 1 (1998)Google Scholar
  18. Rajawat, A., Pandey, M., Rajput, S.: Low resolution face recognition techniques: A survey. In: 2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT) (2017)Google Scholar
  19. Singh, N., Daniel, A., Chaturvedi, P.: Template matching for detection & recognition of frontal view of human face through Matlab. In: 2017 International Conference on Information Communication and Embedded Systems (ICICES) (2017)Google Scholar
  20. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, Vol. 1 (2001)Google Scholar
  21. Viola, P., Jones, M.: Fast and robust classification using asymmetric adaboost and a detector cascade. In: Advances in Neural Information Processing Systems, pp. 1311–1318 (2002)Google Scholar
  22. Xiao, R., Zhu, L., Zhang, H.: Boosting chain learning for object detection. In: Proceedings Ninth IEEE International Conference on Computer Vision, Vol. 1, pp. 709–715 (2003)Google Scholar
  23. Zhang, Z., Zhu, L., Li, S., Zhang, H.: Real-time multi-view face detection. In: Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition, pp. 149–154 (2002)Google Scholar

Authors and Affiliations

  1. 1.Mathematics, Graphics and Visualization Research Group (MGRAVS), Faculty of Science and Natural ResourcesUniversiti Malaysia SabahKota KinabaluMalaysia