Actas de congresos
Facial Fiducial Points Detection Using Discriminative Filtering On Principal Components
Registro en:
9781424479948
Proceedings - International Conference On Image Processing, Icip. , v. , n. , p. 2681 - 2684, 2010.
15224880
10.1109/ICIP.2010.5651849
2-s2.0-78651101726
Autor
Junior W.S.S.
Araujo G.M.
Da Silva E.A.B.
Goldenstein S.K.
Institución
Resumen
The Discriminative Filtering technique performs pattern recognition using a two-dimensional filter. It has a closed-form design, based on the pattern and the statistics of the image set. Here, we investigate the use of Discriminative Filtering for detecting fiducial points in human faces. We show that designing discriminative filters for the principal components increases robustness. The method is assessed in a fiducial points detection framework using a Gentle AdaBoost classifier. © 2010 IEEE.
2681 2684 Nandy, D., Ben-Arie, J., EXM eigen templates for detecting and classifying arbitrary junctions (1998) Proceedings of the International Conference on Image Processing, pp. 211-215. , Kobe, Japan, October Mendonça, A.P., Da Silva, E.A.B., Multiple template detection using impulse restoration and discriminative filters (2003) IEE Electronics Letters, 39 (16), pp. 1172-1174. , August Mendonça, A.P., Da Silva, E.A.B., Two-dimensional discriminative filters for image template detection (2001) Proceedings of the International Conference on Image Processing, , Thessaloniki, Greece, September Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J., Active shape models-their training and application (1995) Computer Vision and Image Understanding, 61 (1), pp. 38-59 Stefano, A., Paola, C., Raffaella, L., An efficient method to detect facial fiducial points for face recognition (2004) Proceedings of the 17th International Conference on Pattern Recognition, pp. 532-535. , Cambridge, UK, August (2008) The BioID Database, , http://www.humanscan.de/support/downloads/facedb.php Joachims, T., Burges, C., Scholkopf, B., Smola, A., (1999) Advances in Kernel Methods: Support Vector Learning, pp. 169-184. , Eds., chapter Making large-scale support vector machine learning practical, MIT press, Cambridge, MA Mendonça, A.P., Da Silva, E.A.B., Closed-form solutions for discriminative filtering using impulse restoration techniques (2002) IEE Electronics Letters, 38 (22), pp. 1332-1333. , October Naser, A.A., Galatsanos, N.P., Wernick, M.N., Impulse restoration-based template-matching using the expectation-maximization algorithm (1997) Proceedings of the International Conference on Image Processing, pp. 158-161. , Washington, DC, October Naser, A.A., (2000) Impulse Restoration-based Template-matching, , Ph.D. Dissertation, University of Illinois, Chicago, USA Kirby, M., Sirovich, L., Application of the karhunen-loeve procedure for the characterization of human faces (1990) IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 Stan, Z.L., Anil, K.J., (2004) Handbook of Face Recognition, , Springer-Verlag, Secaucus, NJ, USA, 1ed edition Viola, P., Jones, M., Robust real-time object detection (2001) International Journal of Computer Vision, 57 (2), pp. 137-154. , July Xiaoy, T., Triggs, B., Enhanced local texture feature sets for face recognition under difficult lighting conditions (2007) Proceedings of the International Workshop on Analysis and Modeling of Faces and Gestures, pp. 168-182 Jerome, F., Trevor, H., Tibshirani, R., Additive logistic regression: A statistical view of boosting (1998) Annals of Statistics, 28, p. 2000 (2008) The GML AdaBoost Matlab Toolbox, , http://graphics.cs.msu.ru/en/science/research/machinelearning/ adaboosttoolbox