In this paper, we explore the use of computer vision techniques to analyze students' moods during one-on-one teaching interactions. The eventual goal is to create automated tutoring systems that are sensitive to the student's mood and affective state. We find that the problem of accurately determining a child'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.
JF - 2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshops (FG 2011) PB - IEEE CY - Santa Barbara, CA SN - 978-1-4244-9140-7 ER - TY - CONF T1 - Automatic cry detection in early childhood education settings T2 - 7th IEEE International Conference on Development and Learning, 2008. ICDL 2008 Y1 - 2008 A1 - Ruvolo, P. A1 - Movellan, J. KW - Acoustic noise KW - auditory moods KW - automatic cry detection KW - behavioural sciences computing KW - Deafness KW - early childhood education settings KW - education KW - Educational robots KW - Emotion recognition KW - human coders KW - Humans KW - learning (artificial intelligence) KW - Machine Learning KW - Mood KW - preschool classrooms KW - Prototypes KW - Robustness KW - Working environment noise AB -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].
JF - 7th IEEE International Conference on Development and Learning, 2008. ICDL 2008 PB - IEEE CY - Monterey, CA SN - 978-1-4244-2661-4 ER -