基于图像变换和多流CNN的熟练伪造签名识别与检测

Papiya Das, Swarnabja Bhaumik, Subhrapratim Nath
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引用次数: 0

摘要

通过生物特征或签名等凭证进行身份识别对个人隐私非常重要,已成为身份识别最重要的组成部分。近年来,防止手写签名的伪造日益受到重视。为了实现这一目标,本文采用图像变换技术和人工智能模型来有效识别真伪签名的差异。草火变换和光流捕捉到特征的差异。提出的系统使用深度学习框架与ResNet 50以及卷积神经网络(CNN)。采用SVC2004和SUSIG基准与已有文献进行对比研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Signature Recognition and Detection of Skilled Forgeries Using Image Transformation and Multistream CNN
Person identification through their credentials such as biometrics or signature is very important for one’s privacy and has become the most integral part for recognition. Prevention of forgeries in handwritten signatures has gain prominence in recent times. To serve this target this paper carried out Image Transformation techniques and an Artificial Intelligence model to effectively notice the differences of genuine and forged signature. Grass-fire transformations and optical flow captures the disparity in signatures. Proposed system uses Deep learning framework with ResNet 50 along with Convolutional Neural network (CNN). Comparative studies have been done using SVC2004 and SUSIG benchmark with the existing literature.
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