| Visual Recognition of Continuous Hand Postures (2008) | |||||||||||||||
Abstract | |||||||||||||||
| This paper1 describes GREFIT (Gesture REcognition based on FInger Tips), a neural network-based system which recognizes continuous hand postures from grey level video images. 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 3D 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 combined with a-priori knowledge locates the 2D-positions of the fingertips in the image. In the second stage, the 2D-position information is transformed by an artificial neural net into an estimate of the 3D-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 a remarkable accuracy and can follow postures from grey scale images at a frame rate of 10 Hz. 1 | |||||||||||||||
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