Machine Learning in Cybersecurity: Computer Vision for Biometric Authentication Systems

Abstract

The increasing demand for secure authentication mechanisms in a digital world has propelled the use of biometric systems, particularly those leveraging machine learning and computer vision technologies. This paper investigates the application of computer vision and machine learning algorithms in enhancing cybersecurity through automated biometric authentication. Focusing on facial recognition, fingerprint scanning, and other visual data-based techniques, the study explores the methodologies, benefits, and challenges associated with implementing these systems. The efficacy of machine learning algorithms, including convolutional neural networks (CNNs) and support vector machines (SVMs), is examined in relation to their performance in various biometric applications. The findings indicate that integrating advanced computer vision techniques significantly improves the accuracy and reliability of biometric authentication systems. Furthermore, the paper discusses ethical considerations and future directions for research in this evolving field.