Abstract: Sign language recognition is an effective way to break the barriers between communication between deaf and hearing people.Generally,Chinese sign language can be divided into gesture language and finger language.Regional and individual differences lead to a wide variety,therefore gesture language recognition is relatively difficult,which requires constant learning and training.The finger language gives the result through the expression of the Chinese pinyin letters,which is deterministic,especially in terms of name,special meaning,and abstract expression.Most of the researches in sign language recognition concentrate on a certain gesture,focusing on key features such as hand shape,direction,position and motion trajectory,and combine some learning algorithms to improve the recognition accuracy,but neglect the most basic and reliable finger recognition.To this end,an effective method using gray level co-occurrence matrix (GLCM) and fine Gaussian support vector machine (FGSVM) was proposed to solve the problem of identifying Chinese finger sign language more accurately and effectively.The research method is as follows.Firstly,the finger sign language data set was constructed.The finger language image was directly obtained by the digital camera or got from the key frame of the video,meanwhile the hand shape was segmented from the image,and each image was adjusted to N×N specific size and converted to grayscale images.Secondly,feature extraction was performed to reduce the dimension of the intensity values in the grayscale image,and at the same time,the corresponding gray level co-occurrence matrix was created,and the enhanced data features were obtained by adjusting the parameters of inter-pixel distance and angle.Finally,the extracted image feature data were submitted to the fine Gaussian support vector machine classifier based on the 10-fold cross-validation classification.Experiments on 510 Chinese finger sign language image samples from 30 categories show that the classification accuracy based on GLCM-FGSVM is up to 92.7%,and this method can be considered as effective approach in Chinese finger sign language classification.

Key words: Chinese sign language, Fine Gaussian support vector machine, Finger sign language, Gray level co-occurrence matrix, Sign language recognition