Abstract
Face detection is a fundamental research area in computer vision field. Most of the face-related applications such as face recognition and face tracking assume that the face region is perfectly detected. To adopt a certain face detection algorithm in these applications, evaluation of its performance is needed. Unfortunately, it is difficult to evaluate the performance of face detection algorithms due to the lack of universal criteria in the literature. In this paper, we propose a new evaluation measure for face detection algorithms by exploiting a biological property called Golden Ratio of the perfect human face. The new evaluation measure is more realistic and accurate compared to the existing one. Using the proposed measure, five haar-cascade classifiers provided by Intel©OpenCV have been quantitatively evaluated on three common databases to show their robustness and weakness as these classifiers have never been compared among each other on same databases under a specific evaluation measure. A thoughtful comparison between the best haar-classifier and two other face detection algorithms is presented. Moreover, we introduce a new challenging dataset, where the subjects wear the headscarf. The new dataset is used as a testbed for evaluating the current state of face detection algorithms under the headscarf occlusion.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Green C.D.: All that glitters: a review of psychological research on the aesthetics of the golden section. Perception 24, 937–968 (1995)
Hassaballah M., Kanazawa T., Ido S., Ido S.: Efficient eye detection method based on grey intensity variance and independent components analysis. IET Comput. Vis. 4(4), 261–271 (2010)
Hjelmas E., Low B.K.: Face detection: a survey. Comput. Vis. Image Underst. 83(31), 236–274 (2001)
Intel-Cooperation: Intel open source computer vision libaray (OpenCV) (2011). Available at http://www.sourceforge.net/projects/opencvlibrary/
Jesorsky O., Kirchberg K., Frischholz K.: Robust face detection using the hausdorff distance. LNCS 2091, 90–95 (2001)
Kienzle, W., Bakir, G., Franz, M., Scholkopf, B.: Face detection efficient and rank deficient. In: Weiss, Y. (ed.) Advances in Neural Information Processing Systems, vol. 17, pp. 673–680. MIT Press, Cambridge, MA, USA (2005)
Lienhart, R., Kuranov, A., Pisarevsky, V.: Empirical analysis of detection cascades of boosted classifiers for rapid object detection. In: Proceedings of the 25th Pattern Recognition Symposium (DAGM’03), Germany, pp. 297–304 (2003)
Livio M.: The Golden Ratio: the Story of Phi, the World’s most Astonishing Number. Broadway Books, New York (2002)
Meynet J., Popovici V., Thiran J.P.: Face detection with boosted Gaussian features. Pattern Recogn. 40(8), 2283–2291 (2007)
Powell, N., Humphreys, B.: Proportions of the aesthetic face. Thieme-Stratton, New York, ISBN:0865771170 (1984)
Rowley H.A., Baluia S., Kanade T.: Neural network-based face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20(1), 22–38 (1998)
Shih P., Liu C.: Face detection using discriminating feature analysis and support vector machine. Pattern Recogn. 39(2), 260–276 (2006)
Viola P., Jones M.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)
Walser, H.: The golden section. Peter Hilton Trans., The Mathematical Association of America, ISBN:0883855348 (2001)
Xiaohua L., Lam K.M., Lansun S., Jiliu Z.: Face detection using simplified Gabor features and hierarchical regions in a cascade of classifiers. Pattern Recogn. Lett. 30(8), 717–728 (2009)
Yang M.H., Kriegman D.J., Ahuja N.: Detecting faces in images: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 24(1), 34–58 (2002)
Yann R., Fabien C., Sammy B., Johnny M.: Measuring the performance of face localization systems. Image Vis. Comput. 24(8), 882–893 (2006)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Hassaballah, M., Murakami, K. & Ido, S. Face detection evaluation: a new approach based on the golden ratio \({\Phi}\) . SIViP 7, 307–316 (2013). https://doi.org/10.1007/s11760-011-0239-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-011-0239-3