论文标题
卷积长的短期记忆网络损坏的指纹识别，用于法医目的
Damaged Fingerprint Recognition by Convolutional Long Short-Term Memory Networks for Forensic Purposes
论文作者
论文摘要
指纹识别通常是建立针对罪犯的证据的改变一步。但是，我们越来越多地发现，犯罪分子故意以多种方式改变其指纹，以使技术人员和自动传感器很难识别其指纹，这使调查人员在法医程序中建立反对他们的有力证据。从这个意义上讲，深度学习是协助识别受损指纹的主要候选人。特别是卷积算法。在本文中，我们关注卷积长的短期记忆网络对受损指纹的识别。我们介绍了模型的体系结构，并证明其性能超过95％的精度，99％的精度，接近95％的召回和99％的AUC。
Fingerprint recognition is often a game-changing step in establishing evidence against criminals. However, we are increasingly finding that criminals deliberately alter their fingerprints in a variety of ways to make it difficult for technicians and automatic sensors to recognize their fingerprints, making it tedious for investigators to establish strong evidence against them in a forensic procedure. In this sense, deep learning comes out as a prime candidate to assist in the recognition of damaged fingerprints. In particular, convolution algorithms. In this paper, we focus on the recognition of damaged fingerprints by Convolutional Long Short-Term Memory networks. We present the architecture of our model and demonstrate its performance which exceeds 95% accuracy, 99% precision, and approaches 95% recall and 99% AUC.
