@inproceedings{0334c2232e9042b092d83a71746f3bda,
title = "Age Estimation System Using Deep Residual Network Classification Method",
abstract = "The human face has biometric properties that are important for providing age information because of the aging process of the face. Automatic Age estimation is a difficult problem because the relationship between facial images and age is not very linear. Deep residual network (Resnet) is a neural network convolution architecture that was easier to optimize and can gain accuracy results from a considerably increasing depth. In this paper, we propose a new approach age estimation on convolution neural network (CNN) using the deep residual network (Resnet) model. Through the literature, Resnet achieves superior results when compared with other state-of-the-art image classifications. We compare a new generation of deep residual network called ResNeXt with Resnet and a basic linier regression model architecture.We user UTKFace dataset to evaluate the performance of residual network for age estimation of the range 1-100 years old. The result shows that the ResNeXt-50 (32×4d) architecture achieves a better age estimation results than Resnet-50 and linier regression.",
keywords = "ResNeXt, age estimation, deep residual network, human face",
author = "Arna Fariza and Mu'Arifin and Arifin, {Agus Zainal}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 21st International Electronics Symposium, IES 2019 ; Conference date: 27-09-2019 Through 28-09-2019",
year = "2019",
month = sep,
doi = "10.1109/ELECSYM.2019.8901521",
language = "English",
series = "IES 2019 - International Electronics Symposium: The Role of Techno-Intelligence in Creating an Open Energy System Towards Energy Democracy, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "607--611",
editor = "Ahmad Zainudin and Yunanto, {Andhik Ampuh}",
booktitle = "IES 2019 - International Electronics Symposium",
address = "United States",
}