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@article{ART002826199},
author={Inki Kim and Beomjun Kim and Woo, Sung-hee and Jeonghwan Gwak},
title={Contactless User Identification System using Multi-channel Palm Images Facilitated by Triple Attention U-Net and CNN Classifier Ensemble Models},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2022},
volume={27},
number={3},
pages={33-43},
doi={10.9708/jksci.2022.27.03.033}

TY - JOUR
AU - Inki Kim
AU - Beomjun Kim
AU - Woo, Sung-hee
AU - Jeonghwan Gwak
TI - Contactless User Identification System using Multi-channel Palm Images Facilitated by Triple Attention U-Net and CNN Classifier Ensemble Models
JO - Journal of The Korea Society of Computer and Information
PY - 2022
VL - 27
IS - 3
PB - The Korean Society Of Computer And Information
SP - 33
EP - 43
SN - 1598-849X
AB - In this paper, we propose an ensemble model facilitated by multi-channel palm images with attention U-Net models and pretrained convolutional neural networks (CNNs) for establishing a contactless palm-based user identification system using conventional inexpensive camera sensors. Attention U-Net models are used to extract the areas of interest including hands (i.e., with fingers), palms (i.e., without fingers) and palm lines, which are combined to generate three channels being ped into the ensemble classifier. Then, the proposed palm information-based user identification system predicts the class using the classifier ensemble with three outperforming pre-trained CNN models. The proposed model demonstrates that the proposed model could achieve the classification accuracy, precision, recall, F1-score of 98.60%, 98.61%, 98.61%, 98.61% respectively, which indicate that the proposed model is effective even though we are using very cheap and inexpensive image sensors. We believe that in this COVID-19 pandemic circumstances, the proposed palm-based contactless user identification system can be an alternative, with high safety and reliability, compared with currently overwhelming contact-based systems.
KW - Palm-based Identification;Contactless Identification System;Multi-channel image;Attention U-Net;Ensemble of Pre-trained CNN Models
DO - 10.9708/jksci.2022.27.03.033
ER -

Inki Kim, Beomjun Kim, Woo, Sung-hee and Jeonghwan Gwak. (2022). Contactless User Identification System using Multi-channel Palm Images Facilitated by Triple Attention U-Net and CNN Classifier Ensemble Models. Journal of The Korea Society of Computer and Information, 27(3), 33-43.

Inki Kim, Beomjun Kim, Woo, Sung-hee and Jeonghwan Gwak. 2022, "Contactless User Identification System using Multi-channel Palm Images Facilitated by Triple Attention U-Net and CNN Classifier Ensemble Models", Journal of The Korea Society of Computer and Information, vol.27, no.3 pp.33-43. Available from: doi:10.9708/jksci.2022.27.03.033

Inki Kim, Beomjun Kim, Woo, Sung-hee, Jeonghwan Gwak "Contactless User Identification System using Multi-channel Palm Images Facilitated by Triple Attention U-Net and CNN Classifier Ensemble Models" Journal of The Korea Society of Computer and Information 27.3 pp.33-43 (2022) : 33.

Inki Kim, Beomjun Kim, Woo, Sung-hee, Jeonghwan Gwak. Contactless User Identification System using Multi-channel Palm Images Facilitated by Triple Attention U-Net and CNN Classifier Ensemble Models. 2022; 27(3), 33-43. Available from: doi:10.9708/jksci.2022.27.03.033

Inki Kim, Beomjun Kim, Woo, Sung-hee and Jeonghwan Gwak. "Contactless User Identification System using Multi-channel Palm Images Facilitated by Triple Attention U-Net and CNN Classifier Ensemble Models" Journal of The Korea Society of Computer and Information 27, no.3 (2022) : 33-43.doi: 10.9708/jksci.2022.27.03.033

Inki Kim; Beomjun Kim; Woo, Sung-hee; Jeonghwan Gwak. Contactless User Identification System using Multi-channel Palm Images Facilitated by Triple Attention U-Net and CNN Classifier Ensemble Models. Journal of The Korea Society of Computer and Information, 27(3), 33-43. doi: 10.9708/jksci.2022.27.03.033

Inki Kim; Beomjun Kim; Woo, Sung-hee; Jeonghwan Gwak. Contactless User Identification System using Multi-channel Palm Images Facilitated by Triple Attention U-Net and CNN Classifier Ensemble Models. Journal of The Korea Society of Computer and Information. 2022; 27(3) 33-43. doi: 10.9708/jksci.2022.27.03.033

Inki Kim, Beomjun Kim, Woo, Sung-hee, Jeonghwan Gwak. Contactless User Identification System using Multi-channel Palm Images Facilitated by Triple Attention U-Net and CNN Classifier Ensemble Models. 2022; 27(3), 33-43. Available from: doi:10.9708/jksci.2022.27.03.033

Inki Kim, Beomjun Kim, Woo, Sung-hee and Jeonghwan Gwak. "Contactless User Identification System using Multi-channel Palm Images Facilitated by Triple Attention U-Net and CNN Classifier Ensemble Models" Journal of The Korea Society of Computer and Information 27, no.3 (2022) : 33-43.doi: 10.9708/jksci.2022.27.03.033