Proceedings of the 1st Workshop on Multidisciplinary and Its Applications Part 1, WMA-01 2018, 19-20 January 2018, Aceh, Indonesia

Research Article

Signature Identification with Gray Level Co-ccurrence Matrix and Extreme Learning Machine

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  • @INPROCEEDINGS{10.4108/eai.20-1-2018.2281923,
        author={Roseri  Sinaga and Tulus  Tulus and Erna  Budhiarti and A M H  Pardede},
        title={Signature Identification with Gray Level Co-ccurrence Matrix and Extreme Learning Machine},
        proceedings={Proceedings of the 1st Workshop on Multidisciplinary and Its Applications Part 1, WMA-01 2018, 19-20 January 2018, Aceh, Indonesia},
        publisher={EAI},
        proceedings_a={WMA-1},
        year={2019},
        month={9},
        keywords={signature identification extreme learning machine gray level co-occurrence matrix},
        doi={10.4108/eai.20-1-2018.2281923}
    }
    
  • Roseri Sinaga
    Tulus Tulus
    Erna Budhiarti
    A M H Pardede
    Year: 2019
    Signature Identification with Gray Level Co-ccurrence Matrix and Extreme Learning Machine
    WMA-1
    EAI
    DOI: 10.4108/eai.20-1-2018.2281923
Roseri Sinaga1,*, Tulus Tulus1, Erna Budhiarti1, A M H Pardede2
  • 1: Department of Computer Science, Universitas Sumatera Utara, Medan, Indonesia
  • 2: STMIK Kaputama, Jl. Veteran No. 4A-9A, Binjai, Sumatera Utara, Indonesia
*Contact email: roseri.sinaga@student.usu.ac.id

Abstract

Identification of the signature is a process used to identify the signature of a person. The Identification of the signature can be divided into two parts. Identification signature off-line and the Identification of signatures on-line. Forgery of signatures is still common in data security systems. So, we need an approach to improve the accuracy of signature recognition on Extreme Learning Machine algorithms. This study use a gray level co - occurrence matrix (GLCM) for feature extraction and modification of Extreme Learning Machine (ELM) for recognition. ELM is a new learning method of a neural network or commonly called the Single Hidden Layer Feed forward Neural Networks (SLFNs). From the experiments the identification of the signature using the gray level co-occurrence matrix (GLCM) and signature classification using extreme learning machine with the addition of elementary transformations showed that the accuracy on identification of the signature is 43% using of features contrast, correlation, energy and homogeneity, while using just ELM accuracy is 36%.