Chinese Sign Language Recognition for a Vision-Based Multi-features Classifier
Quan Yang, Peng Jinye · 2008
According to the global and local features of Chinese manual alphabet images, Fourier descriptor and other multi-features is introduced for the vision-based multi-features classifier of Chinese sign language recognition. At first, extracting features of letter images is done, then classification method of SVMs for recognition is brought into use. Experimentation with 30 groups of the Chinese manual alphabet images is conducted and the results prove that these global and local visual features, such as Fourier descriptors, are simple, efficient, and effective for characterize hand gestures, and the SVMs method has excellent classification and generalization ability in solving learning problem with small training set of sample in sign language recognition. The experimentation shows that linear kernel function is suitable for sign language recognition, and the best recognition rate of 99.4872% of letter ¿F¿ image group is achieved.