Using Gabor Filter Bank with Downsampling and SVM for Visual Sign Language Alphabet Recognition
Galal M. Bin Makhashen, Hamzah Luqman, El-Sayed M. El-Alfy · 2019
With the increasing advances in computer vision, the research on automated human gesture and sign language has attracted the attention of many researchers. It has many applications for human-computer interaction helping persons with hearing impairment in smart environments. In this paper, we focus on static hand visual features to build a system for recognizing hand and finger gestures representing different sign language alphabets. After hand segmentation, the proposed method employs texture based features extracted by down-sampling Gabor-transformed images using multiple scales and orientations. Then, a support vector machine is used for multi-class classification. The evaluation of the proposed approach on a benchmark dataset for the American sign language has reported over 95% overall accuracy with several signs perfectly recognized.