Identifikasi Tanda Tangan dengan Ekstraksi Ciri GLCM dan LBP

yuliana diah pristanti, Panca Mudjirahardjo, Achmad Basuki

Abstract


Signature identification with extraction features of GLCM (The Gray Level Co-occurrence Matrix) and LBP (The Local Binary Pattern) compare the results of both accuracy. By using signatures from 15 people, each of which has 10 signatures. For the training data, 7 signatures from each person were taken so that the training data amounted to 105 signatures. While the testing data was taken 3 signatures from each person so that the test data amounted to 45 signatures. From the results of image processing obtained the percentage using GLCM feature extraction is greater than the percentage using LBP feature extraction, namely GLCM reaches 86.67% and LBP 80.00%. But both remain at a high level of success. So it can be concluded that both GLCM and LBP feature extraction can be recommended to recognize signature textures.

 

Index Terms—GLCM, LBP, Signature.


Full Text:

PDF

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.