Hand gesture recognition based on Bayesian sensing hidden Markov models and Bhattacharyya divergence
Sih-Huei Chen, Ari Hernawan, Yuan-Shan Lee, Jia‐Ching Wang · 2017
This work develops a system for recognizing common hand gestures. The main idea that underlies the developed system is the incorporation of Bhattacharyya divergence into Bayesian sensing hidden Markov models (BS-HMM). The system consists of two stages. First, a sequence of depth images is captured by Microsoft Kinect. The hand region is identified from the depth images by tracking the position of the hand using information about the skeleton, yielding the segmented depth images. A histogram of the oriented normal 4D (HON4D) and a histogram of oriented gradient (HOG) are then extracted from the segmented depth images to represent the motion patterns. Second, all training feature vectors are transformed by combining every k consecutive feature vectors into a sequence of distributions. The proposed Bhattacharyya divergence based BS-HMM (BDBS-HMM) is trained using the sequence of distributions. The proposed system is compared to the standard HMM and the BS-HMM using MSRGesture3D database and our database. Experimental results indicated that the proposed method outperforms the baseline methods.