Advancements in Computer Vision for Biometrics Enhancing Security and Identification
Vivek Upadhyaya · 2024
This chapter investigates the most recent breakthroughs in computer vision as it relates to biometric systems, with a particular emphasis on the important role these innovations play in enhancing security measures and enhancing the efficiency of identification procedures. Because of recent advancements in computer vision, biometrics has become an accurate and time-saving method for determining an individual&s;s identity. Additionally, this method&s;s possibilities have been greatly expanded as a result. This chapter explores a variety of biometric modalities, such as facial recognition, fingerprint analysis, iris scanning, voice recognition, and gait analysis. The focus of the discussion is on how computer vision algorithms extract and process distinguishing information for correct identification. In addition to this, it investigates the incorporation of deep learning strategies, convolutional neural networks (CNNs), and various other approaches to machine learning in order to further improve biometric accuracy and resilience. The significant progress that has been made is acknowledged, but the chapter also discusses the issues that have arisen in relation to potential biases, privacy concerns, and adversarial attacks. In addition to this, it examines the implementation of multi-modal biometrics and fusion techniques in order to enhance the overall performance of the system. In general, the purpose of this chapter is to highlight the revolutionary impact that computer vision may have in biometric applications, which ultimately results in increased levels of security and more effective identification procedures in a variety of real-world settings.