Video analysis for view-based painful expression recognition

In recent years, facial expressions of pain have been the focus of considerable behavioral research. Such work has documented that pain expressions, like other affective facial expressions, play an important role in social communication. Enabling computer systems to recognize pain expression from facial images is a challenging research topic. In this paper, we present two systems for pain recognition from video sequences. The first approach, eigenimage, projects the face images, detected from video sequences, onto a feature space, defined by eigenfaces, to produce the biometric template. Recognition is performed by projecting a new image onto that feature space and then classifying the face by comparing its position in the feature spaces with the positions of known individuals. To ensure better accuracy, the system is tested against two more feature spaces defined by eigeneyes and eigenlips. The second approach, neural network, extracts location and shape features of the detected faces and uses them as inputs to the artificial neural network which employs the standard error back-propagation algorithm for classification of faces. From experiments, we conclude that neural network based method is better in terms of speed and accuracy than eigenimage based method.