Automatic Detection of Facial Feature Points via HOGs and Geometric Prior Models
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.
KeywordsSalient Point Detection Histogram of Oriented Gradients Ensemble learning
Unable to display preview. Download preview PDF.
- 1.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)Google Scholar
- 2.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)Google Scholar
- 3.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)Google Scholar
- 6.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)Google Scholar
- 8.Lepetit, V., Fua, P.: Keypoint Recognition Using Randomized Trees. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1465–1479 (2006)Google Scholar
- 10.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)Google Scholar
- 11.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)Google Scholar
- 13.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)Google Scholar