Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Facial asymmetry quantification for expression invariant human identification
Computer Vision and Image Understanding - Special issue on Face recognition
AdaTree: Boosting a Weak Classifier into a Decision Tree
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 6 - Volume 06
Use of off-line dynamic programming for efficient image interpretation
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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While there has been a great deal of research in face detection and recognition, there has been very limited work on identifying the expression on a face. Many current face detection projects use a [Viola/Jones] style “cascade” of Adaboost-based classifiers to interpret (sub)images — e.g. to identify which regions contain faces. We extend this method by learning a decision tree of such classifiers (dtc): While standard cascade classification methods will apply the same sequence of classifiers to each image, our dtc is able to select the most effective classifier at every stage, based on the outcomes of the classifiers already applied. We use dtc not only to detect faces in a test image, but to identify the expression on each face.