SECURING FINGERPRINT-BASED AUTHENTICATION SYSTEMS BY MASTERPRINT

Fingerprint-based authentication systems are widely used for various applications, but recent research has shown that partial fingerprints can be used to bypass the authentication systems, highlighting the need for a novel approach to enhance their security. In this research proposal, we aim to develop a novel method for enhancing the security of partial fingerprint-based authentication systems using machine learning algorithms. The proposed method involves data collection, feature extraction, and developing a machine learning model to match the partial fingerprints with the previously stored fingerprints. The performance of the proposed method will be evaluated using various metrics, such as accuracy, false acceptance rate, and false rejection rate. This research is expected to contribute to the development of more secure and reliable authentication systems and increase awareness of the potential vulnerabilities of partial fingerprint-based authentication systems.