Publication

Abstract : In this paper, a novel approach for hand pose recognition by using key geometrical features of hand is proposed. A skeletal hand model is constructed to analyze the abduction and adduction movements of the fingers and these variations are modeled by multidimensional probabilistic distributions. For recognizing hand poses, proximity measures are computed between input gestures and pre-modeled gesture patterns. The proposed algorithm is more robust to the improper hand segmentation and side movements of fingers. Experimental results show that the proposed method is very much suitable for the applications related to Human Computer Interactions (HCI).