Investigate the Vulnerabilities and Improvements in Biometric Authentication Systems Using Deep Learning Techniques

The advances in digitization globally, is essential to have efficient and effective system which would enable a secured data communication. The paradigm shift has led to many critical issues which has an impact on the performance of the system. Biometrics is one of the technologies to enhance authentication and security of the information technology systems. The biometric authentication systems have gained widespread adoption in various domains due to their potential to enhance security and user convenience. However, these systems are immune to vulnerabilities, understanding and addressing these weaknesses are crucial for ensuring the reliability of biometric authentication. The biometric technology has to ensure high security and reliability. In the current scenario the biometric verification and validation methods comprises of finger print recognition, iris scanning, facial recognition, voice recognition and other sophisticated approaches. The research focuses on the study and analysis of security vulnerabilities of the biometric technology. The vulnerabilities result in inaccurately identification or failure of identification of the individuals. This contribution focuses on to enhance the performance of the biometric system, we propose a multimodal biometric approach to enable secured and efficient system. Here we propose two approaches which can be applied to validate a biometric system. the fingerprint authentication and Iris scanning using the deep learning technology.