Hand posture recognition from disparity cost map

In this paper, we address the problem of hand posture recognition with a binocular camera. As bare hand has a few landmarks for matching, instead of using accurate matching between two views, we define a kind of mapping score---Disparity Cost Map. The disparity cost map serves as the final hand representation for recognition. As we use the disparity cost map, an explicit segmentation stage is not necessary. Local Binary Pattern (LBP) is used as feature for classification in this paper. In order to align the LBP feature, we further design an annular mask to deal with the problem of scaling, rotation, translation (RST) and search for an accurate bounding box of hand. The experimental results demonstrate the efficiency and robustness of our method. For 15 hand postures in varies cluttered background, the proposed method achieves an average recognition rate of 95% with a SVM classifier.