Abstract
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.
Original language | English |
---|---|
State | Published - 2019 |
Bibliographical note
Publisher Copyright:© 2019 Institution of Engineering and Technology. All rights reserved.
Keywords
- Downsampling
- Gabor Filer Bank
- Gesture Recognition
- Pattern Recognition
- Sign Language
- Support Vector Machine
- Texture analysis
ASJC Scopus subject areas
- Electrical and Electronic Engineering