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Human forehead recognition: a novel biometric modality based on near-infrared laser backscattering feature image using deep transfer learning

机译:人额识别:一种基于深度转移学习的近红外激光后向散射特征图像的新型生物特征识别方法

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摘要

Human recognition systems are an essential tool for identity verification. Though various parts of the human body have been widely used as input data for decades, developing new biometric technology is still necessary to enhance the security system complexity. This article presents a novel biometric modality based on forehead feature images acquired from a specially designed near-infrared laser scanning system. The authors selected state-of-the-art deep convolutional neural networks (CNN), including VGGNet, ResNet, and Inception-v3, to demonstrate the human forehead recognition task. Though large-scale training data is generally required for learning a promising CNN model, they showed the feasibility to transfer the feature representation knowledge of the networks that were pre-trained on the data from a different domain and fine-tuned the target network on the limited dataset of forehead feature images. This transfer learning approach establishes the usability of human forehead recognition and allows us to implement this biometric modality for real-world application.
机译:人类识别系统是身份验证的重要工具。尽管数十年来,人体的各个部位已被广泛用作输入数据,但仍需要开发新的生物识别技术来提高安全系统的复杂性。本文介绍了一种基于从特殊设计的近红外激光扫描系统获取的额头特征图像的新型生物特征识别方式。作者选择了包括VGGNet,ResNet和Inception-v3在内的最先进的深度卷积神经网络(CNN)来演示人类前额识别任务。尽管通常需要大规模的训练数据来学习有前途的CNN模型,但它们显示了可行性,即可以对来自不同域的数据进行预训练的网络的特征表示知识进行传递,并可以对目标网络进行微调。前额特征图像的有限数据集。这种转移学习方法建立了人类前额识别的可用性,并使我们能够为实际应用实现这种生物识别方式。

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