Abstract
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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Isukapalli, R., Elgammal, A., Greiner, R. (2005). Learning a Dynamic Classification Method to Detect Faces and Identify Facial Expression. In: Zhao, W., Gong, S., Tang, X. (eds) Analysis and Modelling of Faces and Gestures. AMFG 2005. Lecture Notes in Computer Science, vol 3723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564386_7
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DOI: https://doi.org/10.1007/11564386_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29229-6
Online ISBN: 978-3-540-32074-6
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