Faces and Gestures
The aforementioned work on representation and learning has contributed to two types of human computer interfaces we have developed. First, learning and classification techniques, including usual statistical classifiers, neural networks, support vector machines and artificial intelligence approaches, have been used to develop new methods for human face detection and hand gesture recognition.
To develop methods to tell the identity of a person from a frontal image and evaluate its performance with state-of-the-art methods.
- Y. Kitamura, J. Ohya, N. Ahuja and F. Kishino, Computational Taxonomy and Recognition of Facial Expressions, First Asian Conference on Computer Vision, November 23-25, 1993, Osaka, Japan, 434-437.
- J. Ma, N. Ahuja, C. Neti and A. Senior, Recovering Frontal-Pose Image from a Single Profile Image, IEEE Int. Conference on Image Processing, Vol. 2, Vancouver, BC, Canada, Sept. 2000, 243-247.
- M.-H. Yang, N. Ahuja and D. Kriegman, Face Recognition Using Kernel Eigenfaces, IEEE Int. Conference on Image Processing, Vol. 1, Vancouver, BC, Canada, Sept. 2000, 37-41.
- M.-H. Yang and N. Ahuja, A Geometric Approach to Train Support Vector Machines, IEEE Int. Conference on Computer Vision and Pattern Recognition (CVPR), Vol. I, Hilton Head, SC, June 2000, 430-437.
- M.H. Yang and N. Ahuja, Face Detection and Hand Gesture Recognition for Vision-Based Human Computer Interaction, Kluwer Academic Publishers, 2001.