optimization fingerprint reconstruction using deep learning algorithm

Ming-Sie Pan, Chao-Hsin Fan, Yih-Lon Lin, Hsiang-Chen Hsu
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引用次数: 0

Abstract

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.
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基于深度学习算法的指纹重建优化
指纹识别是最知名的数字身份识别之一,已广泛应用于法医学、刑事侦查、金融服务、电子智能锁等领域。本文基于Unet方法对潜在指纹进行图像分割和重建。首先,在茚三酮反应热致纸上采集指纹潜痕,利用Unet算法对指纹图像进行分割;其次,对残缺指纹进行完整的圆环和圆环图像重建。最后,采用受试者工作特征(ROC)曲线方案对统计开发模型的分类精度进行了分析。
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