Thai finger-spelling sign language recognition using global and local features with SVM
Thongpan Pariwat, Pusadee Seresangtakul · 2017
This paper presents a finger-spelling recognition system focusing on Thai finger-spelling sign language, derived from the computer vision, using SVM. In this study, global and local features were extracted from input finger images. In order to develop the recognition system, 15 Thai alphabet characters were collected from five hand signers, totally 375 character pictures, in order to train the system using the SVM technique; with linear, polynomial, RBF, and sigmoid kernels. Each kernel method employed three feature vectors extracted from global features, local features, and the combination of both features; and were measured for performance in 4 SVM kernels, with five-fold cross-validation. The experimental results demonstrated that the combination of global and local features applied in RBF, linear, polynomial, and sigmoid resulted in the average accuracies of 91.20%, 86.40%, 80.00%, and 54.67%, respectively. The RBF method with the combination of global and local features provided the highest accuracy among all combinations.