Adaptive Man-Machine Interfaces

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This work will exploit learning techniques recently developed to work towards computers that learn to detect and recognize people, estimate user's gestures and communicate visually with them via a photorealistic computer-generated human face. In particular, the plan is to use tow main theoretical and algorithmic approaches to learning: Support Vector Machines and Hidden Markov Models. With these tools, two key aspects of a trainable man-machine interface will be developed: An analysis module that can be trained to estimate in real time facial expressions of the user and associated physical parameters and a synthesis module that can be trained to generate image sequences of a real human face synchronized to a text-to-speech system. The significance of the work is three-fold: (1) The project will contribute to the development of a new generation of computer interfaces more user-friendly and human-centered than today's interfaces. Such interfaces will be of direct use in education and as components of prostheses for the disadvantaged; (2) The project will integrate recently developed learning techniques to real time vision and graphics applications; and (3) The project will explore the boundaries of what is possible to achieve using 2D representations of faces rather than the more common, physically-based, 3D-based models.