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 - TY - CONF T1 - A discriminative approach to frame-by-frame head pose tracking T2 - 8th IEEE International Conference on Automatic Face Gesture Recognition, 2008. FG '08 Y1 - 2008 A1 - Whitehill, J. A1 - Movellan, Javier R. KW - accuracy drift KW - continuous video sequence KW - controlled illumination condition KW - discriminative approach KW - face detection KW - face recognition KW - facial appearance KW - frame-by-frame head pose tracking KW - Humans KW - Image analysis KW - Image databases KW - Laboratories KW - Lighting KW - Magnetic heads KW - mean square error methods KW - pose estimation KW - Robustness KW - root-mean-square error tracking KW - System testing KW - Video sequences AB -

We present a discriminative approach to frame-by-frame head pose tracking that is robust to a wide range of illuminations and facial appearances and that is inherently immune to accuracy drift. Most previous research on head pose tracking has been validated on test datasets spanning only a small (< 20) subjects under controlled illumination conditions on continuous video sequences. In contrast, the system presented in this paper was both trained and tested on a much larger database, GENKI, spanning tens of thousands of different subjects, illuminations, and geographical locations from images on the Web. Our pose estimator achieves accuracy of 5.82deg, 5.65deg, and 2.96deg root-mean-square (RMS) error for yaw, pitch, and roll, respectively. A set of 4000 images from this dataset, labeled for pose, was collected and released for use by the research community.

JF - 8th IEEE International Conference on Automatic Face Gesture Recognition, 2008. FG '08 PB - IEEE CY - Amsterdam SN - 978-1-4244-2153-4 ER -