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首页> 外文期刊>Applied Sciences >Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning
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Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning

机译：使用卷积神经网络转移学习进行掌纹验证的深层感兴趣区域和特征提取模型

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摘要

Palmprint verification is one of the most significant and popular approaches for personal authentication due to its high accuracy and efficiency. Using deep region of interest (ROI) and feature extraction models for palmprint verification, a novel approach is proposed where convolutional neural networks (CNNs) along with transfer learning are exploited. The extracted palmprint ROIs are fed to the final verification system, which is composed of two modules. These modules are (i) a pre-trained CNN architecture as a feature extractor and (ii) a machine learning classifier. In order to evaluate our proposed model, we computed the intersection over union (IoU) metric for ROI extraction along with accuracy, receiver operating characteristic (ROC) curves, and equal error rate (EER) for the verification task.The experiments demonstrated that the ROI extraction module could significantly find the appropriate palmprint ROIs, and the verification results were crucially precise. This was verified by different databases and classification methods employed in our proposed model. In comparison with other existing approaches, our model was competitive with the state-of-the-art approaches that rely on the representation of hand-crafted descriptors. We achieved a IoU score of 93% and EER of 0.0125 using a support vector machine (SVM) classifier for the contact-based Hong Kong Polytechnic University Palmprint (HKPU) database. It is notable that all codes are open-source and can be accessed online.
机译：掌纹验证由于其高精度和高效率，是最重要的个人身份验证方法之一。使用感兴趣的深区域（ROI）和特征提取模型进行掌纹验证，提出了一种新颖的方法，其中利用了卷积神经网络（CNN）和传递学习。提取的掌纹ROI被馈送到最终验证系统，该系统由两个模块组成。这些模块是（i）作为特征提取器的预训练CNN架构，以及（ii）机器学习分类器。为了评估我们提出的模型，我们计算了用于ROI提取的联合交集（IoU）度量以及验证任务的准确性，接收器工作特性（ROC）曲线和等错误率（EER）。 ROI提取模块可以显着找到合适的掌纹ROI，并且验证结果至关重要。通过我们提出的模型中使用的不同数据库和分类方法对此进行了验证。与其他现有方法相比，我们的模型与依靠手工描述子表示的最新方法相比具有竞争力。使用基于接触的香港理工大学掌上电脑（HKPU）数据库的支持向量机（SVM）分类器，我们获得了93％的IoU评分和0.0125的EER。值得注意的是，所有代码都是开源的，可以在线访问。

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