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Finger vein recognition based on PCA and fusion convolutional neural network | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 13، شماره 1، خرداد 2022، صفحه 3667-3681 اصل مقاله (546.14 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.6145 | ||
| نویسندگان | ||
| Mohammed S. H. Al-Tamimi1؛ Ruaa S. S. AL-Khafaji* 2 | ||
| 1Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq | ||
| 2Computer Science Department, College of Science, University of Baghdad, Baghdad, Iraq | ||
| تاریخ دریافت: 10 مرداد 1400، تاریخ بازنگری: 17 شهریور 1400، تاریخ پذیرش: 28 مهر 1400 | ||
| چکیده | ||
| Finger vein recognition and user identification is a relatively recent biometric recognition technology with a broad variety of applications, and biometric authentication is extensively employed in the information age. As one of the most essential authentication technologies available today, finger vein recognition captures our attention owing to its high level of security, dependability, and track record of performance. Embedded convolutional neural networks are based on the early or intermediate fusing of input. In early fusion, pictures are categorized according to their location in the input space. In this study, we employ a highly optimized network and late fusion rather than early fusion to create a Fusion convolutional neural network that uses two convolutional neural networks (CNNs) in short ways. The technique is based on using two similar CNNs with varying input picture quality, integrating their outputs in a single layer, and employing an optimized CNN design on a proposed Sains University Malaysia (FV-USM) finger vein dataset 5904 images. The final pooling CNN, which is composed of the original picture, an image improved using the contrast limited adaptive histogram (CLAHE) approach and the Median filter, And, using Principal Component Analysis (PCA), we retrieved the features and got an acceptable performance from the FV-USM database, with a recognition rate of 98.53 percent. Our proposed strategy outperformed other strategies described in the literature. | ||
| کلیدواژه‌ها | ||
| Finger vein؛ CNN؛ CLAHE؛ Median Filter؛ PCA؛ FV-USM | ||
| مراجع | ||
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