Abstract
Several methods have been developed for face detection with certain success, however these tend to fail under difficult conditions such as partial occlusions and changes in orientation and illumination. We propose a novel technique for face detection based on a visual grammar. We define a symbol relational grammar for faces, representing the visual elements of a face and their spatial relations. This grammar is transformed into a Bayesian network (BN) structure and its parameters are obtained from data, i.e., positive and negative examples of faces. Then the BN is used for face detection via probabilistic inference, using as evidence a set of weak detectors for different face components. We evaluated our method on a set of sample images of faces under difficult conditions, and contrasted it with a simplified model without relations, and the AdaBoost face detector. The results show a significant improvement when using our method.
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References
Candido, J., Marengoni, M.: Combining information in a bayesian network for face detection. Brazilian Journal of Prob. Stat. 23, 179–195 (2009)
Ferrucci, F., Pacini, G., Satta, G., Sessa, M., Tortora, G., Tucci, M., Vitiello, G.: Symbol-relation grammars: A formalism for graphical languages. Information and Computation 131, 1–46 (1996)
Han, F., Zhu, S.-C.: Bottom-up/top-down image parsing by attribute graph grammar. In: IEEE International Conference on Computer Vision, vol. 2, pp. 1778–1785 (2005)
Neapolitan, R.E.: Learning Bayesian Networks. Pearson, Prentice Hall, Chicago, Illiniois (2004)
Nefian, A.V.: A hidden Markov model based approach for face detection and recognition. PhD dissertation, Georgia Institute of Technology, Georgia, USA (1999)
Pearl, J.: Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San Francisco (1988)
Viola, P., Jones, M.J.: Robust real-time face detection. International Journal of Computer Vision 57, 137–154 (2004)
Wu, T.-F., Xia, G.-S., Zhu, S.-C.: Compositional boosting for computing hierarchical image structures. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Yu, Q., Cheng, H.H., Cheng, W.W., Zhou, X.: Ch opencv for interactive open architecture computer vision. Adv. Eng. Softw. 35(8-9), 527–536 (2004)
Zhao, W.Y., Chellappa, R., Philips, P.J., Rosenfeld, A.: Face recognition: A literature survey. ACM Computing Survey, 399–458 (2003)
Zhu, S.-C., Mumford, D.: A stochastic grammar of images. Found. Trends. Comput. Graph. Vis. 2(4), 259–362 (2006)
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Meléndez, A., Sucar, L.E., Morales, E.F. (2010). A Visual Grammar for Face Detection. In: Kuri-Morales, A., Simari, G.R. (eds) Advances in Artificial Intelligence – IBERAMIA 2010. IBERAMIA 2010. Lecture Notes in Computer Science(), vol 6433. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16952-6_50
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DOI: https://doi.org/10.1007/978-3-642-16952-6_50
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