Investigation on the Performance of Haar Cascade Classifier to Classify Images Using OpenCV

Muhammad Bahit, Nadia Putri Utami, Heru Kartika Candra, Hafizi Al Madhani, Noprianto

2022

Abstract

Face is an essential part of human identity that others can recognize directly. As technology develops, face is used as a tool for human interaction with computers in various fields. However, face recognition has many problems such as accuracy, a long process, and recognition errors due to the clothes worn. Therefore, various methods have been developed to overcome facial recognition problems, including the haar cascade classifier method. Therefore, this study aims to investigate the performance of the haar cascade classifier method in classifying images using OpenCV. This study used six datasets with 300 images in each dataset to test the accuracy in classyifing images. The results found that the performance accuracy of the haar cascade classifier method in classifying images increased in each epoch, from 0.2211 in the 1st epoch to 0.7755 in the 10th epoch. In addition, the validation accuracy also had an increase from 0.2492 in the 1st epoch to 0.7803 in the 10th epoch. Thus, this study suggests using the haar cascade classifier method in image classification for detection face.

Paper Citation

in Harvard Style

Bahit M., Utami N., Candra H., Madhani H. and Noprianto. (2022). Investigation on the Performance of Haar Cascade Classifier to Classify Images Using OpenCV. In Proceedings of the 5th International Conference on Applied Science and Technology on Engineering Science - Volume 1: iCAST-ES; ISBN 978-989-758-619-4, SciTePress, pages 313-316. DOI: 10.5220/0011765500003575

in Bibtex Style

@conference{icast-es22,
author={Muhammad Bahit and Nadia Putri Utami and Heru Kartika Candra and Hafizi Al Madhani and Noprianto},
title={Investigation on the Performance of Haar Cascade Classifier to Classify Images Using OpenCV},
booktitle={Proceedings of the 5th International Conference on Applied Science and Technology on Engineering Science - Volume 1: iCAST-ES},
year={2022},
pages={313-316},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011765500003575},
isbn={978-989-758-619-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 5th International Conference on Applied Science and Technology on Engineering Science - Volume 1: iCAST-ES
TI - Investigation on the Performance of Haar Cascade Classifier to Classify Images Using OpenCV
SN - 978-989-758-619-4
AU - Bahit M.
AU - Utami N.
AU - Candra H.
AU - Madhani H.
AU - Noprianto.
PY - 2022
SP - 313
EP - 316
DO - 10.5220/0011765500003575
PB - SciTePress