Optimization fingerprint reconstruction using deep learning algorithm

被引：0

作者：

Pan, Ming-Sie ^{[1
]}

Fan, Chao-Hsin ^{[2
]}

Lin, Yih-Lon ^{[3
]}

Hsu, Hsiang-Chen ^{[1
]}

机构：

[1] I Shou Univ, Dept Ind Management, Kaohsiung, Taiwan

[2] Tainan City Police Dept, Tainan, Taiwan

[3] Natl Yunlin Univ Sci & Technol, Dept Informat Engn, 1,Sec 1,Syuecheng Rd, Kaohsiung 84001, Taiwan

来源：

2022 17TH INTERNATIONAL MICROSYSTEMS, PACKAGING, ASSEMBLY AND CIRCUITS TECHNOLOGY CONFERENCE (IMPACT) | 2022年

关键词：

fingerprint recognition; fingerprint marks; Unet; Ninhydrin reaction; Receiver Operating Characteristic (ROC) curve;

D O I：

10.1109/IMPACT56280.2022.9966693

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Fingerprint recognition is one of the most well-known digital identifications and has been widely used on forensic science, criminal investigation, financial services, electronic smart locks...etc. In this paper, latent fingerprint marks have been image segmentation and reconstruction based on the Unet method. In the first, latent fingerprint marks were collected on Ninhydrin reaction thermal-induced paper and the image of fingerprints were segmented using Unet algorithm. Secondly, mutilated fingerprints were image reconstructed for the whole loops and whorls on a finger. And lastly, a Receiver Operating Characteristic (ROC) curves scheme has been applied to analyzed classification accuracy of a statistical developed model.

引用

页数：3

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