HAND POSTURE RECOGNITION USING APPROXIMATE VANISHING IDEAL GENERATORS

This paper represents a hand posture recognition method that combines both skin and shape cues. In the algorithm, implicit skin image is firstly computed to suppress the background disturbances as well as to enhance the hand region; and vanishing component analysis (VCA) algorithm is applied to each posture category to learn a group of Approximate Vanishing Ideal Generators (AVIGs) which are used for the follow-up feature extraction. Each generator is essentially an effective characterization for geometrical structure of corresponding hand posture. In recognition phase, features acquired from implicit skin image and AVIGs are inputted to a soft-max model for classification. A dataset comprising 5 hand postures are constructed for its evaluation. The proposed algorithm is demonstrated to be robust to complex environment and challenging illuminations.