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http://dx.doi.org/10.9708/jksci.2022.27.03.033

Contactless User Identification System using Multi-channel Palm Images Facilitated by Triple Attention U-Net and CNN Classifier Ensemble Models  

Kim, Inki (Dept. of IT.Energy Convergence, Korea National University of Transportation)
Kim, Beomjun (Dept. of IT.Energy Convergence, Korea National University of Transportation)
Woo, Sunghee (Dept. of Computer Engineering, Korea National University of Transportation)
Gwak, Jeonghwan (Dept. of Software, Korea National University of Transportation)
Publication Information
Journal of the Korea Society of Computer and Information / v.27, no.3, 2022 , pp. 33-43 More about this Journal
Abstract
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.
Keywords
Palm-based Identification; Contactless Identification System; Multi-channel image; Attention U-Net; Ensemble of Pre-trained CNN Models;
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Times Cited By KSCI : 4  (Citation Analysis)
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