Abstract
Most applications dealing with problems involving the face require a robust estimation of the facial salient points. Nevertheless, this estimation is not usually an automated preprocessing step in applications dealing with facial expression recognition. In this paper we present a simple method to detect facial salient points in the face. It is based on a prior Point Distribution Model and a robust object descriptor. The model learns the distribution of the points from the training data, as well as the amount of variation in location each point exhibits. Using this model, we reduce the search areas to look for each point. In addition, we also exploit the global consistency of the points constellation, increasing the detection accuracy. The method was tested on two separate data sets and the results, in some cases, outperform the state of the art.
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
Cohn, J.F., Sayette, M.A.: Spontaneous facial expression in a small group can be automatically measured: An initial demonstration. Behavior Research Methods (in press)
Cristinacce, D., Cootes, T.: Feature detection and tracking with constrained local models. In: Proceedings of British Machine Vision Conference, vol. 3, pp. 929–938 (2006)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 886–893 (2005)
Hastie, J.F.T., Tibshirani, R.: Additive logistic regression: A statistical view of boosting. The Annals of Statistics 28(2), 337–374 (2000)
Jesorsky, O., Kirchberg, K., Frischholz, R.: Robust face detection using the hausdorff distance. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 90–95. Springer, Heidelberg (2001)
Kanade, T., Cohn, J.F., Tian, Y.: Comprehensive database for facial expression analysis. In: Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 46–53 (2000)
Kozakaya, T., Shibata, T., Yuasa, M., Yamaguchi, O.: Facial feature localization using weighted vector concentration approach. Image and Vision Computing 28(5), 772–780 (2010)
Lepetit, V., Fua, P.: Keypoint Recognition Using Randomized Trees. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1465–1479 (2006)
Mayer, C., Wimmer, M., Radig, B.: Adjusted pixel features for robust facial component classification. Image and Vision Computing 28(5), 762–771 (2010)
Shinohara, Y., Otsuf, N.: Facial expression recognition using fisher weight maps. In: Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 499–504 (2004)
Valstar, M., Martinez, B., Binefa, X., Pantic, M.: Facial point detection using boosted regression and graph models. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2729–2736 (2010)
Viola, P., Jones, M.: Robust real-time object detection. International Journal of Computer Vision 57(2), 137–154 (2002)
Vukadinovic, D., Pantic, M.: Fully Automatic Facial Feature Point Detection Using Gabor Feature Based Boosted Classifiers. In: IEEE International Conference on Systems, Man and Cybernetics (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rojas Quiñones, M., Masip, D., Vitrià, J. (2011). Automatic Detection of Facial Feature Points via HOGs and Geometric Prior Models. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds) Pattern Recognition and Image Analysis. IbPRIA 2011. Lecture Notes in Computer Science, vol 6669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21257-4_46
Download citation
DOI: https://doi.org/10.1007/978-3-642-21257-4_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21256-7
Online ISBN: 978-3-642-21257-4
eBook Packages: Computer ScienceComputer Science (R0)