Facial Fiducial Points Detection Using Discriminative Filtering On Principal Components

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.26812684Nandy, 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, OctoberMendonça, 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. , AugustMendonça, 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, SeptemberCootes, 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. 38-59Stefano, 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.phpJoachims, 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, MAMendonça, 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. , OctoberNaser, 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, OctoberNaser, A.A., (2000) Impulse Restoration-based Template-matching, , Ph.D. Dissertation, University of Illinois, Chicago, USAKirby, M., Sirovich, L., Application of the karhunen-loeve procedure for the characterization of human faces (1990) IEEE Transactions on Pattern Analysis and Machine Intelligence, 12Stan, Z.L., Anil, K.J., (2004) Handbook of Face Recognition, , Springer-Verlag, Secaucus, NJ, USA, 1ed editionViola, P., Jones, M., Robust real-time object detection (2001) International Journal of Computer Vision, 57 (2), pp. 137-154. , JulyXiaoy, 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. 168-182Jerome, 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/ adaboosttoolbo