Survey Paper on 3-D Hand Geometry Based Recognition System for User Authentication Using Image Processing

Abstract

User authentication is a critical aspect of modern security systems, ranging from personal devices to secure facilities. Traditional authentication methods often rely on passwords, PINs, or biometric features like fingerprints or facial recognition. However, these methods can be vulnerable to unauthorized access or spoofing. This paper presents a novel approach to user authentication using 3D hand geometry-based recognition, leveraging image processing techniques. A Palm print, biometric characteristics, was mostly found in civil and commercial applications for security system because it has more reliable and easy to capture by low resolution devices. This research focuses on the development of hand identification and hand geometry using hand features, including the length of the hand, length and width of each finger, size of palm. We use radius distance methods to find the position of the fingertip and the concave of the finger from the hand contour. The radius distance method is highly flexible, accurately detecting the curves of fingertip and concave of finger. We use these reference points to identify the characteristics of individual hands. The sample images are acquired from the simple and low-cost acquisition system. The experimental results demonstrate the efficiency of the proposed method. 3D shape reconstruction from multiple hand-drawn sketches is an intriguing way to 3D shape modelling. Currently, state-of-the-art methods employ neural networks to learn a mapping from multiple sketches from arbitrary view angles to a 3D voxel grid. Because of the cubic complexity of 3D voxel grids, however, neural networks are hard to train and limited to low resolution reconstructions, which leads to a lack of geometric detail and low accuracy. To resolve this issue, we propose to reconstruct 3D shapes from multiple sketches using direct shape optimization (DSO), which does not involve deep learning models for direct voxel-based 3D shape generation. Specifically, we first leverage a conditional generative adversarial network (CGAN) to translate each sketch into an attenuance image that captures the predicted geometry from a given viewpoint. Then, DSO minimizes a project-and-compare loss to reconstruct the 3D shape such that it matches the predicted attenuance images from the view angles of all input sketches. Based on this, we further propose a progressive update approach to handle inconsistencies among a few hand-drawn sketches for the same 3D shape. Our experimental results show that our method significantly outperforms the state-of-the-art methods under widely used benchmarks and produces intuitive results in an interactive application.