Adaptive Man-Machine Interfaces

这项工作将利用最近开发的学习技术，使计算机能够学习检测和识别人，估计用户的手势，并通过逼真的计算机生成的人脸与他们进行视觉交流。 特别是，该计划将使用两种主要的理论和算法方法来学习：支持向量机和隐马尔可夫模型。 有了这些工具，一个可训练的人机界面的两个关键方面将被开发：一个分析模块，可以被训练来估计在真实的时间的面部表情的用户和相关的物理参数和合成模块，可以被训练来生成图像序列的真实的人脸同步到文本到语音系统。 这项工作的意义有三个方面：（1）该项目将有助于开发新一代的计算机界面，比今天的界面更友好和以人为本。 这些接口将直接用于教育，并作为弱势群体假肢的组成部分;（2）该项目将把最近开发的学习技术与真实的视觉和图形应用相结合;（3）该项目将探索使用二维面部表示而不是更常见的基于物理的三维模型可能实现的边界。

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.