In this paper, we present a new approach to recognizing hand signs. In this approach, motion recognition (the hand movement) is tightly coupled with spatial recognition (hand shape). The system uses multiclass, multidimensional discriminant analysis to automatically select the most discriminating linear features for gesture classi cation. A recursive partition tree approximator is proposed to do classi cation. This approach combined with our previous work on hand segmentation forms a new framework which addresses the three key aspects of hand sign interpretation: the hand shape, the location, and the movement. The framework has been tested to recognize 28 di erent hand signs. The experimental results show that the system achieved a 93.2% recognition rate for test sequences that have not been used in the training phase. It is shown that our approach provides better performance than the nearest neighbor classi cation in the eigen-subspace.
REF used normalized segmented hand images as features and reported a 93.2% recognition rate on 28 different signs.
Appearance-Based Hand Sign Recognition from Intensity Image Sequences
Comput. Vis. Image Underst.
Comput. Vis. Image Underst.
Computer Science Mathematics