Learning to Identify Facial Expression During Detection Using Markov Decision Process

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 methods use a Viola–Jones style “cascade” of Adaboost-based classifiers to detect faces. We demonstrate that faces with similar expression form “clusters” in a “classifier space” defined by the real-valued outcomes of these classifiers on the images and address the the task of using these classifiers to classify a new image into the appropriate cluster (expression). We formulate this as a Markov Decision Process and use dynamic programming to find an optimal policy — here a decision tree whose internal nodes each correspond to some classifier, whose arcs correspond to ranges of classifier values, and whose leaf nodes each correspond to a specific facial expression, augmented with a sequence of additional classifiers. We present empirical results that demonstrate that our system a...

• Type: Conference
• Year: 2006
• Where: FGR
• Authors: Ramana Isukapalli, Ahmed M. Elgammal, Russell Greiner
• Keywords: Face Detection, Face Detection Methods, FGR 2006, Similar Expression Form