The Use of Attention-RNN and Dense Layer Combinations and The Performance Metrics Achieved in Palm Vein Recognition

Indriani, Indriani and Syukriyah, Yenie (2025) The Use of Attention-RNN and Dense Layer Combinations and The Performance Metrics Achieved in Palm Vein Recognition. Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, 11 (1). pp. 12-26.

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Abstract

The utilization of palm veins in vascular biometrics is widely recognized, offering significant potential and challenges for advancing individual recognition technology. Deep learning has played a crucial role in enhancing the accuracy of these recognition systems. In this study, we proposed combining Attention-RNN and Dense Layer. To validate this proposed method, three deep learning model scenarios were implemented: (1) a combined Dense Layer with RNN, (2) an Attention-RNN model, and (3) a combined Attention-RNN with a Dense Layer for palm vein recognition. Experimental results demonstrated that the Attention-RNN combined with the Dense Layer achieved the highest accuracy, outperforming the other two models. The model’s performance was evaluated on two datasets, achieving 95% accuracy on the Kaggle dataset and 83% on the CASIA dataset, confirming its effectiveness in palm vein recognition.

Item Type:	Artikel Umum
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisi / Prodi:	Faculty of Industrial Technology (Fakultas Teknologi Industri) > S1-Electrical Engineering (S1-Teknik Elektro)
Depositing User:	M.Eng. Alfian Ma'arif
Date Deposited:	08 Jul 2025 01:37
Last Modified:	08 Jul 2025 01:37
URI:	http://eprints.uad.ac.id/id/eprint/84739

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