Enhancing Personal Identification Security for Improved Accuracy and Reliability
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Abstract
Biometric technologies offer enhanced security and increased accuracy in safeguarding personal identifications and addressing highly secure identification challenges. The utilization of this technology has gained prominence in recent years as a result of the increase in transaction frauds, security breaches, and issues related to personal identification. Biometric technology offers the advantage of generating a distinct code for each individual, hence rendering it impervious to replication or forgery by unauthorized parties. These systems are gaining significant traction in the interconnected world, displacing traditional methods such as passwords and keys due to their enhanced dependability, distinctiveness, and the growing need for heightened security. Currently, fingerprint biometric systems are commonly utilized. However, there are numerous opportunities to replicate an individual's fingerprints without their knowledge. Therefore, it is possible to engage in fraudulent activities by creating counterfeit documents in order to gain unauthorized access to his personal accounts, computers, and even withdraw cash from automated teller machines (ATMs), among other actions. In this paper, we present a proposed personal identification system that utilizes palm prints as a means of overcoming the limitations associated with fingerprint identification systems. Palm print identification is a burgeoning research field within biometric identification systems, offering distinct advantages such as uniqueness, scalability, expedited execution speed, and ample surface area for feature extraction. The method offers enhanced security compared to fingerprint biometric systems because to its extensive range of properties, including wrinkles, continuous ridges, primary lines, minutiae points, and unique points. The primary objective of the proposed palm print identification system is to develop and deploy a system that achieves enhanced accuracy and improved speed in the identification of palm prints belonging to multiple users. In this study, we propose a robust palm print identification system that prioritizes security. Our approach involves extracting the region of interest (ROI) using morphological operations and applying the un- decimated bi-orthogonal wavelet (UDBW) transform to extract low-level features from registered palm prints. These features are then used to calculate feature vectors (FV). Finally, we compare the registered palm feature vector with the testing palm print feature vector by measuring their distance. The simulation findings demonstrate that the biometric identification system presented in this study exhibits enhanced accuracy and a higher degree of reliability in terms of recognition rate.
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