Multiscale Approach in Deep Convolutional Networks for Minutia Extraction from Contactless Fingerprint Images

Anderson Nogueira Cotrim, H. Pedrini
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引用次数: 1

Abstract

Biometric identification by contactless fingerprinting has been a trend in recent years, reinforced by the pandemic of the new coronavirus (COVID-19). Contactless acquisition tends to be a more hygienic acquisition category with greater user acceptance because it is less invasive and does not require the use of a surface touched by other people as traditional acquisition does. However, this area presents some challenging tasks. Contact-based sensors still generally provide greater biometric effectiveness since the minutiae are more pronounced due to the high contrast between ridges and valleys. On the other hand, contactless images typically have low contrast, so the methods fail with spurious or undetectable details, demonstrating the need for further studies in this area. In this work, we propose and analyze a robust scaled deep learning model for extracting minutiae in contactless fingerprint images. The results, evaluated on three datasets, show that the proposed method is competitive against other minutia extraction algorithms and commercial software.
非接触式指纹图像细节提取的深度卷积多尺度方法
近年来,非接触式指纹识别已成为一种趋势,新型冠状病毒(COVID-19)的流行加强了这一趋势。非接触式获取往往是一种更卫生的获取类别,用户接受度更高,因为它侵入性更小,不需要像传统获取那样使用其他人触摸的表面。然而,这一领域提出了一些具有挑战性的任务。基于接触式的传感器通常仍然提供更大的生物识别效率,因为由于山脊和山谷之间的高对比度,细节更加明显。另一方面,非接触式图像通常具有低对比度,因此该方法在虚假或不可检测的细节中失败,表明需要在该领域进行进一步研究。在这项工作中,我们提出并分析了一种鲁棒的缩放深度学习模型,用于提取非接触式指纹图像中的细节。在三个数据集上的评估结果表明,该方法与其他细节提取算法和商业软件相比具有竞争力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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