Robust hand gesture analysis and application in gallery browsing

This paper presents a robust hand gesture analysis method using 3D depth data. Our scheme focuses on accurate hand segmentation by eliminating the negative effect of the forearm part. In the general human computer interaction (HCI) tasks, such an assumption usually holds that the depth of hand is smaller than forearm. Therefore, the precise hand region can be obtained through the fusion of the hand geometric features and the 3D depth information in real-time. Moreover, a robust hand gesture recognition method, which combines the global structure information and the local texture variation, is included in our gesture analysis framework. The elaborate hand segmentation makes the succedent recognition problem much easier and gets more accurate recognition results. Experimental results convincingly show the effectiveness of the proposed gesture analysis strategy. Furthermore, a concrete application scenario, gesture controlled picture gallery browsing, is implemented successfully.