Abstract
Face detection is a research area in computer vision that has received much attention in recent years and a lot of commercial applications were developed using this technology. Even though several methods have been developed, there are still some particular situations that need improvements, especially those related to variations in illumination and face occlusions. In general, illumination problems are handled by using preprocessing, and model or training-based approaches. Here, we propose a face detection method that combines well-known AdaBoost with Gradientfaces technique following a model-based approach, which was not yet used for the face detection problem. We applied Gradientfaces before training an AdaBoost Haar-based cascade classifier to overcome the problem of strong variations in illumination. Quoted approaches were evaluated in two different datasets, containing first artificial and then real illumination problems. Experiments show that proposed method is stable when facing different lighting conditions, and better than others when dealing with strong and uncontrolled illumination problems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Roth, D., hsuan Yang, M., Ahuja, N.: A snow-based face detector. In: Advances in Neural Information Processing Systems, vol. 12, pp. 855–861. MIT Press (2000)
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, pp. I–511–I–518 (2001)
Viola, P., Jones, M.: Robust real-time face detection. International Journal of Computer Vision 57, 137–154 (2004)
Phillips, P., Moon, H., Rizvi, S., Rauss, P.: The feret evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)
Zhang, T., Tang, Y.Y., Fang, B., Shang, Z., Liu, X.: Face recognition under varying illumination using gradientfaces. Trans. Img. Proc. 18(11), 2599–2606 (2009)
Du, B., Shan, S., Qing, L., Gao, W.: Empirical comparisons of several preprocessing methods for illumination insensitive face recognition. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005), pp. ii/981 – ii/984 (March 2005)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: European Conference on Computational Learning Theory, pp. 23–37 (1995)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting (1997)
Horn, B.K.P.: Robot Vision. MIT electrical engineering and computer science series. MIT Press, Cambridge (1986)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Magalhaes, J.P., Ren, T.I., Cavalcanti, G.D.C. (2012). Face Detection under Illumination Variance Using Combined AdaBoost and Gradientfaces. In: Yin, H., Costa, J.A.F., Barreto, G. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2012. IDEAL 2012. Lecture Notes in Computer Science, vol 7435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32639-4_53
Download citation
DOI: https://doi.org/10.1007/978-3-642-32639-4_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32638-7
Online ISBN: 978-3-642-32639-4
eBook Packages: Computer ScienceComputer Science (R0)