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Author: search.filters.author.Adhan, SuchinSubject: search.filters.subject.Geometric invariant feature

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    Thai sign language recognition by using geometric invariant feature and ANN classification
    (2017-02-21)
    Adhan, Suchin
    ;
    Pintavirooj, Chuchart
    Hand sign language is the primary communication tool for people with hearing-impaired or deaf. People can use it to communicate effectively but the challenge is to communicate with the computer. Human computer interaction (HCI) will have a positive impact on their use. Thus, this is to bring the hand gestures in HCI as an important research area. This research focuses on 2D image recognition utilizing an evolved geometric invariant feature and also have developed a two-layer feedforward neural network to identify and translate hand gesture pose of the 42 letters in the Thai Sign Language (TSL) alphabet to Thai alphabets. We designed glove with six different colored markers for using in the experiment. The result shows that this system is able to recognize 42 TSL alphabets with an average accuracy of 96.19 %.
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    American Sign Language recognition by using 3D geometric invariant feature and ANN classification
    (2014-01-20)
    Tangsuksant, Watcharin
    ;
    Adhan, Suchin
    ;
    Pintavirooj, Chuchart
    Communication between normal and disabled person has been developed in several researches. The hand gesture is one of important communication for the deaf, especially American Sign Language (ASL) which is used in order to represent each alphabet (A-Z). This paper aims to translate ASL from static postures. Besides, this research also designs the glove with six different colored markers and develops algorithm for alphabet classification. Moreover the system is set by two cameras in order to extract 3D coordinate points from each marker. There are three main important processes for algorithm consisting of marker detection by using Circle Hough Transform, computation of all feasible triangle area patches constructed from 3D coordinate triplet that is novel feature, and feature classification using feedforward backpropagation of Artificial Neural Network. The experimental result shows average of accuracy is 95 percent that is high performance and feasibility for proposed method.