4 Introduction Why this is a difficult problem? Facial Expressions, Illumination Changes, Pose, etc.Assumption: Frontal view facesObjectives:Develop a fully automatic system, suitable for real-time applications.Evaluate it on a large dataset.
5 FERET DataSet 1196 different individuals Probe Sets: FB: Different facial expressionsFC: Different illumination conditionsDUP1: Different daysDUP2: Images taken at least 1 year after
6 Face Detection State-of-the-art: Learning-based approaches Neural Nets [Rowley et al, PAMI 98]SVMs [Heisele and Poggio, CVPR 01]Boosting [Viola and Jones, ICCV 01]Want to know more?Detecting Faces in Images: a Survey [M. Yang, PAMI 02]
7 Face Detection [Viola and Jones, 2001] Simple features, which can be computed very fast.A variant of Adaboost is used both to select the features and to train the classifier.Classifiers are combined in a “cascade” which allows background regions of the image to be quickly discarded.
20 Conclusions Future Work: An efficient, fully automatic system for face recognition was presented and evaluated.Future Work:Alignment: multiresolution searchView-based face recognitionExplicit illumination modelLive demo