In this paper, we explore the use of computer vision techniques to analyze students{\textquoteright} moods during one-on-one teaching interactions. The eventual goal is to create automated tutoring systems that are sensitive to the student{\textquoteright}s mood and affective state. We find that the problem of accurately determining a child{\textquoteright}s mood from a single video frame is surprisingly difficult, even for humans. However when the system is allowed to make decisions based on information from 10 to 30 seconds of video, excellent performance may be obtained.
}, keywords = {automated facial affect analysis, automated tutoring system, behavioural sciences computing, computer vision technique, Context, decision making, education, Emotion recognition, face recognition, Human, human computer interaction, Labeling, Machine Learning, Mood, n Histograms, one-on-one tutoring application, s Intelligent tutoring systems, student mood analysis}, isbn = {978-1-4244-9140-7}, author = {Butko, N. and Theocharous, G. and Philipose, M. and Movellan, J.} } @conference {64, title = {Learning to Make Facial Expressions}, booktitle = {IEEE 8th International Conference on Development and Learning, 2009. ICDL 2009}, year = {2009}, month = {06/2009}, publisher = {IEEE}, organization = {IEEE}, address = {Shanghai}, abstract = {This paper explores the process of self-guided learning of realistic facial expression production by a robotic head with 31 degrees of freedom. Facial motor parameters were learned using feedback from real-time facial expression recognition from video. The experiments show that the mapping of servos to expressions was learned in under one-hour of training time. We discuss how our work may help illuminate the computational study of how infants learn to make facial expressions.
}, keywords = {Actuators, Emotion recognition, face detection, face recognition, facial motor parameters, Feedback, Humans, learning (artificial intelligence), Machine Learning, Magnetic heads, Pediatrics, real-time facial expression recognition, Robot sensing systems, robotic head, Robots, self-guided learning, Servomechanisms, Servomotors}, isbn = {978-1-4244-4117-4}, author = {Wu, T. and Butko, N. and Ruvulo, P. and Bartlett, M. and Movellan, J.} } @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.} } @conference {60, title = {Automatic cry detection in early childhood education settings}, booktitle = {7th IEEE International Conference on Development and Learning, 2008. ICDL 2008}, year = {2008}, month = {08/2008}, publisher = {IEEE}, organization = {IEEE}, address = {Monterey, CA}, abstract = {We present results on applying a novel machine learning approach for learning auditory moods in natural environments [1] to the problem of detecting crying episodes in preschool classrooms. The resulting system achieved levels of performance approaching that of human coders and also significantly outperformed previous approaches to this problem [2].
}, keywords = {Acoustic noise, auditory moods, automatic cry detection, behavioural sciences computing, Deafness, early childhood education settings, education, Educational robots, Emotion recognition, human coders, Humans, learning (artificial intelligence), Machine Learning, Mood, preschool classrooms, Prototypes, Robustness, Working environment noise}, isbn = {978-1-4244-2661-4}, author = {Ruvolo, P. and Movellan, J.} }