Feature Extraction Method GLCM and LVQ in Digital Image-Based Face Recognition

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T. Sukma Achriadi Sukiman Saib Suwilo Muhammad Zarlis
Corresponding Author:
T. Sukma Achriadi Sukiman
adimarlago@Gmail.com

Copyright (C):
T. Sukma Achriadi Sukiman, Saib Suwilo, Muhammad Zarlis,

Abstract

The face is one of the media to identify someone, a human face has a very high level of variability. Many methods have been introduced by researchers and scientists in recognizing one's face, one of the methods introduced is the Feature Extraction of Gray Level Co-Occurrence Matrix (GLCM) and Learning Vector Quantization (LVQ). GLCM feature extraction is used for data extraction/learning process whereas a data analysis process (face recognition, cropping and storing data) the LVQ method is used for the data training process where the data that has been processed in GLCM feature extraction which still has large dimensions are processed to be smaller dimensions. So this test uses data of 190 photos and gets a match of 90%, the authors conclude that the GLCM feature extraction and LVQ method can very well recognize faces contained in the database.

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How to Cite
SUKIMAN, T. Sukma Achriadi; SUWILO, Saib; ZARLIS, Muhammad. Feature Extraction Method GLCM and LVQ in Digital Image-Based Face Recognition. SinkrOn, [S.l.], v. 4, n. 1, p. 1-4, sep. 2019. ISSN 2541-2019. Available at: <https://jurnal.polgan.ac.id/index.php/sinkron/article/view/10199>. Date accessed: 29 mar. 2020. doi: https://doi.org/10.33395/sinkron.v4i1.10199.
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