Tran Nghi C, Wang Jian-Hong, Vu Toan H, Tai Tzu-Chiang, Wang Jia-Ching
Department of Computer Science and Information Engineering, National Central University, Taoyuan City, Taiwan.
School of Computer Science and Technology, Shandong University of Technology, Zibo, 255049 Shandong China.
J Supercomput. 2023;79(3):2767-2782. doi: 10.1007/s11227-022-04680-4. Epub 2022 Aug 22.
Metaverse, which is anticipated to be the future of the internet, is a 3D virtual world in which users interact via highly customizable computer avatars. It is considerably promising for several industries, including gaming, education, and business. However, it still has drawbacks, particularly in the privacy and identity threads. When a person joins the metaverse via a virtual reality (VR) human-robot equipment, their avatar, digital assets, and private information may be compromised by cybercriminals. This paper introduces a specific Finger Vein Recognition approach for the virtual reality (VR) human-robot equipment of the metaverse of the Metaverse to prevent others from misappropriating it. Finger vein is a is a biometric feature hidden beneath our skin. It is considerably more secure in person verification than other hand-based biometric characteristics such as finger print and palm print since it is difficult to imitate. Most conventional finger vein recognition systems that use hand-crafted features are ineffective, especially for images with low quality, low contrast, scale variation, translation, and rotation. Deep learning methods have been demonstrated to be more successful than traditional methods in computer vision. This paper develops a finger vein recognition system based on a convolution neural network and anti-aliasing technique. We employ/ utilize a contrast image enhancement algorithm in the preprocessing step to improve performance of the system. The proposed approach is evaluated on three publicly available finger vein datasets. Experimental results show that our proposed method outperforms the current state-of-the-art methods, improvement of 97.66% accuracy on FVUSM dataset, 99.94% accuracy on SDUMLA dataset, and 88.19% accuracy on THUFV2 dataset.
元宇宙有望成为互联网的未来，它是一个3D虚拟世界，用户通过高度可定制的计算机化身进行交互。它对包括游戏、教育和商业在内的多个行业都极具潜力。然而，它仍然存在缺点，尤其是在隐私和身份方面。当一个人通过虚拟现实（VR）人机设备加入元宇宙时，他们的化身、数字资产和私人信息可能会被网络犯罪分子窃取。本文介绍了一种针对元宇宙虚拟现实（VR）人机设备的特定手指静脉识别方法，以防止他人盗用。手指静脉是隐藏在我们皮肤下的生物特征。在身份验证方面，它比指纹和掌纹等其他基于手部的生物特征要安全得多，因为它很难被模仿。大多数使用手工特征的传统手指静脉识别系统效果不佳，尤其是对于低质量、低对比度、尺度变化、平移和旋转的图像。在计算机视觉中，深度学习方法已被证明比传统方法更成功。本文基于卷积神经网络和抗混叠技术开发了一种手指静脉识别系统。我们在预处理步骤中采用了一种对比度图像增强算法来提高系统性能。该方法在三个公开可用的手指静脉数据集上进行了评估。实验结果表明，我们提出的方法优于当前的先进方法，在FVUSM数据集上准确率提高了97.66%，在SDUMLA数据集上准确率为99.94%，在THUFV2数据集上准确率为88.19%。
