@conference {72, title = {Automated facial affect analysis for one-on-one tutoring applications}, 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 = {

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.} }