Title :
Comparison study of different feature classifiers for hand posture classification
Author :
Jeonghyun Baek ; Jisu Kim ; Euntai Kim
Author_Institution :
Sch. of Electr. & Electron. Eng., Yonsei Univ., Seoul, South Korea
Abstract :
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.
Keywords :
feature extraction; gesture recognition; gradient methods; human computer interaction; image classification; lighting; neural nets; pose estimation; support vector machines; Cambridge hand gesture dataset; HCI; HOG; LBP; SVM; feature classifiers; hand posture classification; human-computer interaction; illumination change; neural network; pose variation; vision based approach; Biology; Kernel; Polynomials; Rocks; Solid modeling; Three-dimensional displays; Training; HOG; Hand posture classification; LBP; Neural network; SVM;
Conference_Titel :
Control, Automation and Systems (ICCAS), 2013 13th International Conference on
Conference_Location :
Gwangju
Print_ISBN :
978-89-93215-05-2
DOI :
10.1109/ICCAS.2013.6703956
