Anti-aliasing convolution neural network of finger vein recognition for virtual reality (VR) human–robot equipment of metaverse

Anti-aliasing convolution neural network of finger vein recognition for virtual reality (VR) human–robot equipment of metaverse

연구 분야: Cryptography

학회: The Journal of Supercomputing

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.