CVPR 2012 Workshop on Gesture Recognition June 17

Jeffrey Cohn, University of Pittsburgh, Pennsylvania, USA. Non-verbal communication and facial expression. Jeffrey Cohn is Professor of Psychology at the University of Pittsburgh and Adjunct Faculty at the Robotics Institute at Carnegie Mellon University. He has led interdisciplinary and inter-institutional efforts to develop adanced methods of automatic analysis of facial expression and prosody; and applied those tools to research in human emotion, social development, non-verbal communication, psychopathology, and biomedicine. He co-chaired the 2008 IEEE International Conference on Automatic Face and Gesture Recognition (FG2008) and the2009 International Conference on Affective Computing and Intelligent Interaction (ACII2009). He has co-edited two recent special issues of the Journal of Image and Vision Computing. His research has been supported by grants from the National Institutes of Health, National Science Foundation, Autism Foundation, Office of Naval Research, Defense Advanced Research Projects Agency, and the Technical Support Working Group.

Takeo Kanade, Carnegie Mellon University, Pennsylvania, USA. Body motion detection and understanding using both 2D and 3D setups. Body motion detection and understanding using both 2D and 3D setups. Takeo Kanade is the U. A. and Helen Whitaker University Professor of Computer Science and Robotics and the director of Quality of Life Technology Engineering Research Center at Carnegie Mellon University. He works in multiple areas of robotics: computer vision, multi-media, manipulators, autonomous mobile robots, medical robotics and sensors. He has written more than 300 technical papers and reports in these areas, and holds more than 20 patents. He has been the principal investigator of more than a dozen major vision and robotics projects at Carnegie Mellon.

Adam Kendon, University of Pennsylvania, USA. Semiotic analysis of gestures. Adam Kendon is a leading authority on the study of gesture but has also published pioneering studies on the organization of behaviour in face-to-face interaction. In a scholarly career that extends over more than forty years, Kendon has published over one hundred articles and several books all of which deal, in various ways, with the the role of the body in face-to-face interaction. He is, at present, a visiting scholar at the Institute for Research in Cognitive Science at the University of Pennsylvania. He is Editor of the international journal Gesture that has been published by John Benjamins of Amsterdam since 2001 and editor of the companion book series Gesture Studies. He was made an Honorary President of the International Society of Gesture Studies in 2005.

Deva Ramanan, UC Irvine, California, USA, Statistical models for activity recognition. Deva Ramanan graduated with a PhD from UC Berkeley, where he was advised by David Forsyth. Before arriving at UCI, he spent two years as a research professor at TTI-Chicago. He was a visting researcher at the Visual Geometry Group at Oxford, the Robotics Institute at CMU, and the Interactive Visual Media Group at Microsoft Research. His work is primarily in computer vision, but he is also interested in machine learning and computer graphics. His awards include a PASCAL VOC Lifetime Achievement Prize for Object Recognition (invited talk at ICML 2010)and the 2009 Marr Prize for Discriminative Models in Vision.

Sebastian Nowozin, Microsoft Research, Cambridge, UK. Getting quality labeled data for gesture recognition. Sebastian Nowozin's research interest is in developing optimization and machine learning techniques suitable for solving high-level computer vision tasks, such as image classification, object and gesture recognition. High-level computer vision tasks are a unique source of hard machine learning problems for three reasons. First, in contrast to physics-based processes we do not know the correct model (model uncertainty). Second, humans excel at all high-level vision tasks and therefore can provide data and assess model performance (ground truth oracle). Third, image and video data is available for free at an enormous scale (data availability). Sebastian Novozin uses mathematical optimization as a tool to solve computer vision machine learning tasks. He has recently conducted a comparison of HMMs and decision forests for gesture recognition. In this work, he will present an in-depth investigation of the intricacies of getting high quality labeled data.

Thad Starner, Georgia Tech, Georgia, USA. Gesture recognition and human computer interaction. Thad Starner is the director of the Contextual Computing Group. His group creates computational interfaces and agents for use in everyday mobile environments. They combine wearable and ubiquitous computing technologies with techniques from the fields of artificial intelligence (AI), pattern recognition, and human computer interaction (HCI). Recently, they have been designing assistive technology with the deaf community. One of our main projects is CopyCat, a game which uses American Sign Language recognition to help young deaf children acquire language skills. They continually develop new interfaces for mobile computing (and mobile phones) with an emphasis on gesture. Currently, they are exploring mobile interfaces that are fast to access, like wristwatches.

Fernando de la Torre, Carnegie Mellon Univ., Pennsylvania, USA. Unsupervised and weakly supervised discovery of events for human sensing. Fernando De la Torre is an Associate Research Professor in the Robotics Institute at Carnegie Mellon University. He received his B.Sc. degree in Telecommunications, as well as his M.Sc. and Ph. D degrees in Electronic Engineering from La Salle School of Engineering at Ramon Llull University, Barcelona, Spain in 1994, 1996, and 2002, respectively. His research interests are in the fields of Computer Vision and Machine Learning. Specifically, he is interested in modeling and recognizing human behavior with a focus on understanding human behavior from multimodal sensors (e.g. video, body sensors). He has done extensive work on facial image analysis (e.g., facial expression recognition, facial feature tracking). In machine learning his interest centers on developing efficient and robust supervised and unsupervised methods to model high-dimensional data. Currently, he is directing the Component Analysis Laboratory (http://ca.cs.cmu.edu) and the Human Sensing Laboratory (http://humansensing.cs.cmu.edu) at Carnegie Mellon University. He has over 100 publications in referred journals and conferences. He has organized and co-organized several workshops and has given tutorials at international conferences on the use and extensions of Component Analysis.