Abstract
Wrist detection is a crucial element in the hand-pose estimation and hand-gesture recognition processes in Human-Computer Interaction applications. Most methods use horizontal parallel lines to scan for the location of a wrist line. The challenging problems in wrist detection are determining the orientation and localising the horizontal parallel lines that scan for various hand poses. The proposed method automatically detects a wrist, based on a minimum bounding box and geometric features. It also determines the start and stop points to localise the scanning. The evaluation used a set of 1240 hand images with ground-truth data taken from three sets of data. The hand images contained several gestures and individuals to prove that the method is robust against various gestures. The evaluation shows that the method successfully detects the image orientation and the wrist points with high accuracy.
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