@conference {73, title = {The computer expression recognition toolbox (CERT)}, booktitle = {2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshops (FG 2011)}, year = {2011}, month = {03/2011}, publisher = {IEEE}, organization = {IEEE}, address = {Santa Barbara, CA}, abstract = {

We present the Computer Expression Recognition Toolbox (CERT), a software tool for fully automatic real-time facial expression recognition, and officially release it for free academic use. CERT can automatically code the intensity of 19 different facial actions from the Facial Action Unit Coding System (FACS) and 6 different prototypical facial expressions. It also estimates the locations of 10 facial features as well as the 3-D orientation (yaw, pitch, roll) of the head. On a database of posed facial expressions, Extended Cohn-Kanade (CK+[1]), CERT achieves an average recognition performance (probability of correctness on a two-alternative forced choice (2AFC) task between one positive and one negative example) of 90.1\% when analyzing facial actions. On a spontaneous facial expression dataset, CERT achieves an accuracy of nearly 80\%. In a standard dual core laptop, CERT can process 320 {\texttimes} 240 video images in real time at approximately 10 frames per second.

}, keywords = {3D orientation, Accuracy, automatic real-time facial expression recognition, CERT, computer expression recognition toolbox, Detectors, dual core laptop, Emotion recognition, Encoding, extended Cohn-Kanade, Face, face recognition, facial action unit coding system, facial expression dataset, Facial features, FACS, Gold, Image coding, software tool, software tools, two-alternative forced choice task}, isbn = {978-1-4244-9140-7}, author = {Littlewort, G. and Whitehill, J. and Wu, T. and Fasel, I. and Frank, M. and Movellan, J. and Bartlett, M.} } @conference {58, title = {Auditory mood detection for social and educational robots}, booktitle = {IEEE International Conference on Robotics and Automation, 2008. ICRA 2008}, year = {2008}, month = {05/2008}, publisher = {IEEE}, organization = {IEEE}, address = {Pasadena, CA}, abstract = {

Social robots face the fundamental challenge of detecting and adapting their behavior to the current social mood. For example, robots that assist teachers in early education must choose different behaviors depending on whether the children are crying, laughing, sleeping, or singing songs. Interactive robotic applications require perceptual algorithms that both run in real time and are adaptable to the challenging conditions of daily life. This paper explores a novel approach to auditory mood detection which was born out of our experience immersing social robots in classroom environments. We propose a new set of low-level spectral contrast features that extends a class of features which have proven very successful for object recognition in the modern computer vision literature. Features are selected and combined using machine learning approaches so as to make decisions about the ongoing auditory mood. We demonstrate excellent performance on two standard emotional speech databases (the Berlin Emotional Speech [W. Burkhardt et al., 2005], and the ORATOR dataset [H. Quast, 2001]). In addition we establish strong baseline performance for mood detection on a database collected from a social robot immersed in a classroom of 18-24 months old children [J. Movellan er al., 2007]. This approach operates in real time at little computational cost. It has the potential to greatly enhance the effectiveness of social robots in daily life environments.

}, keywords = {auditory mood detection, Computer vision, educational robot, Educational robots, Emotion recognition, emotional speech database, face detection, hearing, interactive robotic application, learning (artificial intelligence), Machine Learning, Mood Prototypes, object recognition, Robotics and Automation Robots, social mood, social robot, Speech, USA Councils}, isbn = {978-1-4244-1646-2}, author = {Ruvolo, P. and Fasel, I. and Movellan, J.} }