Comparison study of different feature classifiers for hand posture classification
Jeonghyun Baek, Jisu Kim, Euntai Kim · 2013
Hand posture classification has attracted much attention in Human-Computer Interaction (HCI). In hand posture classification, vision based approach is popularly used. However, it has difficulty of dealing with illumination change and pose variation. In this paper, we compare the performance of combination with features, which are HOG, LBP, and classifiers, which are SVM and Neural Network for hand posture classification. Experiments are performed with Cambridge hand gesture dataset.