ENHANCING FINGER OUTER KNUCKLES RECOGNITION USING DEEP RECURRENT NEURAL NETWORK

被引：0
|
作者：
Al-Nima, Raid Rafi Omar [1 ]
Al-Askari, Abdulrahman W. H. [1 ]
Othman, Karam M. Z. [1 ]
Eesee, Abdulrahman K. [1 ,2 ]
机构：
[1] Northern Tech Univ, Mosul, Iraq
[2] Univ Pannonia, Dept Proc Engn, ELKH PE Complex Syst Monitoring Res Grp, Veszprem, Hungary
来源：
JOURNAL OF ENGINEERING SCIENCE AND TECHNOLOGY | 2023年 / 18卷 / 06期
关键词：
Biometric; Deep recurrent neural network; Finger outer knuckle; Recognition; IDENTIFICATION; FEATURES;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
In recent years, Finger Outer Knuckle (FOK) has come out as a promising biometric modality. This paper considers enhancing the FOK recognition of verification by using a suggested efficient deep learning network. It is called the Deep Recurrent Neural Network (DRNN). This network has the ability to deal with both minor and major FOKs. It consists of input layer, hidden layers, output layer and global feedback. It can further increase the verification performance. Images of minor and major FOKs from the Indian Institute of Technology Delhi Finger Knuckle (IITDFK) dataset are employed. The result demonstrates promising accuracy verification rate of 96% after utilizing both major and minor FOKs.
引用
收藏
页码：2915 / 2927
页数：13
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