Visual recognition of continuous hand postures.

This paper describes GREFIT (Gesture REcognition based on FInger Tips), a neural network-based system which recognizes continuous hand postures from gray-level video images (posture capturing). Our approach yields a full identification of all finger joint angles (making, however, some assumptions about joint couplings to simplify computations). This allows a full reconstruction of the three-dimensional (3-D) hand shape, using an articulated hand model with 16 segments and 20 joint angles. GREFIT uses a two-stage approach to solve this task. In the first stage, a hierarchical system of artificial neural networks (ANNs) combined with a priori knowledge locates the two-dimensional (2-D) positions of the finger tips in the image. In the second stage, the 2-D position information is transformed by an ANN into an estimate of the 3-D configuration of an articulated hand model, which is also used for visualization. This model is designed according to the dimensions and movement possibilities of a natural human hand. The virtual hand imitates the user's hand to an remarkable accuracy and can follow postures from gray scale images at a frame rate of 10 Hz.