Definition
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.
Introduction
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 via an institution to check access.
References
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)
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)
Cristinacce, D., Cootes, T.: Facial feature detection using AdaBoost with shape constraints. In: Proceedings of the British Machine Vision Conference 2003 (2003)
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)
Devrari, K., Kumar, K.: Fast face detection using graphics processor. Int. J. Comput. Sci. Inf. Technol. 2, 1082–1086 (2011)
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)
Hjelmås, E., Low, B.: Face detection: a survey. Comput. Vis. Image Underst. 83, 236–274 (2001)
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)
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)
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)
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)
Lienhart, R., Maydt, J.: An extended set of Haar-like features for rapid object detection. In: Proceedings. International Conference on Image Processing (2002)
Mahalingam, G., Ricanek, K., Albert, A.: Investigating the Periocular-based face recognition across gender transformation. IEEE Trans. Inf. For. Secur. 9, 2180–2192 (2014)
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)
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)
Modi, M., Macwan, F.: Face detection approaches: a survey. Int. J. Innov. Res. Sci. Eng. Technol. 3, 11107–11116 (2014)
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)
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)
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)
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)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this entry
Cite this entry
Sivaraja, T., Bade, A. (2019). Digital Images Using Heuristic AdaBoost Haar Cascade Classifier Model, Detection of Partially Occluded Faces. In: Lee, N. (eds) Encyclopedia of Computer Graphics and Games. Springer, Cham. https://doi.org/10.1007/978-3-319-08234-9_371-1
Download citation
DOI: https://doi.org/10.1007/978-3-319-08234-9_371-1
Received:
Accepted:
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08234-9
Online ISBN: 978-3-319-08234-9
eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering