Automatic feature extraction for Vietnamese sign language recognition using support vector machine

This paper aims at finding an automatic approach for extracting features of the Vietnamese sign language to classify both static Vietnamese alphabet letters and their combing diacritic marks as dynamic hand gestures. A Vietnamese sign language recognition system (VSLRS) collects all images including depth images, RGB images, and skeletal join maps to extract the desired features of each hand gesture and their own movements. These characteristics are normalized and converted to build a full Vietnamese sign language combing diacritic marks. The primary features of this system are automatically extracting the hand gestures of the observed person before the Kinect device version 1, and both dynamic and static diacritic marks are able to be recognized because of the movement detection method. Multi-class support vector machines (SVMs) and the One-Against-All approach are employed to find two suitable SVMs for static and dynamic hand gesture recognition. During the recognition phase, all hand gestures are extracted, normalized, and then filtered out based on the Euclidean distance difference of hand positions in captured frames to go through the exact SVMs. The recognized letter or diacritic is the positive label of all the SVM classes. The experimental results demonstrate the proposed VSLRS recognized the Vietnamese sign language (VSL) in realtime with the high accuracy.