Generation and Detection of Biometric Authentication Images Using AI: A Comparative Analysise

Abstract

One of the most secure types of authentications is biometric one and it has been still quite appraised during these years because it is reliable and performant and answers correctly. The biggest development in authenticating systems also comes from the evolution in artificial intelligence (AI) technology, specifically through the implementation of deep learning techniques, all but transforming the creation and recognition of biometric photos. Here we perform an extensive comparison of multiple AI-powered methods implemented for the generation and recognition of biometric photoIDs. First the research looks into generation by investigating GANs and VAEs to generate biometric images. And GANs, highly known for their ability to generate realistic images, have thus been widely researched in generating biometric traits (e.g. finger prints, iris scan, face photo and etc...). However, VAEs offer a statistical model of the latent space which could be manipulated to generate different, realistic biometric pictures, keeping the property features within control. The study also investigated the part of detection, particularly how convolutional and recurrent neural networks can be used for biometric image verification Convolutional Neural Networks (CNNs) possess the ability to extract features from images of biometric data, providing a clear background upon which an individual can be authenticated with precision of the obtained features. Thanks to their known ability to learn sequences, Recurrent Neural Networks (RNNs) have been also widely adopted to model temporal dependencies in biometric data. This makes authentication systems better resistant to spoofing attacks. Additionally, the review evaluates specific AI methods in comparison to the generation quality, detection accuracy, computation efficiency and adversarial vulnerability. Furthermore, the research discusses the moral concerns with respect to synthetic biometric data fabrication and utilization, emphasizing the importance of privacy and security in biometric authentication systems. The purpose of this paper is to study the most recent methods for generating and recognizing biometric authentication images using artificial intelligence. The point of the analysis is to offer real-world authentic biometric experience in order to guide researchers and practitioners in developing and deploying secure and reliable biometric authentication systems for various applications.