el 18: e1

Research Article

Chinese fingerspelling recognition via gray-level co-occurrence matrix and fuzzy support vector machine

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  • @ARTICLE{10.4108/eai.12-10-2020.166554,
        author={Yalan Gao and Cheng Xue and Ran Wang and Xianwei Jiang},
        title={Chinese fingerspelling recognition via gray-level co-occurrence matrix and fuzzy support vector machine},
        journal={EAI Endorsed Transactions on e-Learning: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={EL},
        year={2020},
        month={10},
        keywords={Chinese fingerspelling recognition, gray-level co-occurrence matrix, fuzzy support vector machine, principal component analysis},
        doi={10.4108/eai.12-10-2020.166554}
    }
    
  • Yalan Gao
    Cheng Xue
    Ran Wang
    Xianwei Jiang
    Year: 2020
    Chinese fingerspelling recognition via gray-level co-occurrence matrix and fuzzy support vector machine
    EL
    EAI
    DOI: 10.4108/eai.12-10-2020.166554
Yalan Gao1, Cheng Xue1, Ran Wang1, Xianwei Jiang1,*
  • 1: School of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing 210038, China
*Contact email: jxw@njts.edu.cn

Abstract

INTRODUCTION: Chinese deaf-mutes communicate in their native language, Chinese sign language which contains gesture language and finger language. Chinese finger language conveys information through various movements of fingers, and its expression is accurate and convenient for classification and recognition. OBJECTIVES: In this paper, we proposed a new model using gray-level co-occurrence matrix (GLCM) and fuzzy support vector machine (FSVM) to improve sign language recognition accuracy. METHODS: Firstly, we acquired the sign language images directly by a digital camera or selected key frames from the video as the data set, meanwhile, we segmented the hand shapes from the background. Secondly, we adjusted the size of each images to N×N and then switched them into gray-level images. Thirdly, we reduced the dimension of the intensity values by using the Principal Component Analysis (PCA) and acquired the data features by creating the gray-level co-occurrence matrix. Finally, we sent the extracted and reduced dimensionality features to Fuzzy Support Vector Machine (FSVM) to conduct the classification tests. RESULTS: Moreover, we compared it with similar algorithms, and the result shows that our method performs the highest classification accuracy which is up to 86.7%. CONCLUSION: The experiment result displays that our model performs well in Chinese finger language recognition and has potential for further research.