Volume 14 , Issue 2 , PP: 263-274, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
B. R. Sathishkumar 1 * , K. M. Monica 2 , D. Sasikala 3 , M. N. Sudha 4
Doi: https://doi.org/10.54216/JCIM.140218
In today's digital era, ensuring robust and secure authentication mechanisms is crucial. Multi-fusion biometric authentication systems have emerged as a powerful solution to enhance security and reliability by integrating multiple biometric traits. This paper presents a novel Multi-Fusion Biometric Authentication approach using Minutiae-Driven Fixed-Size Template Matching (MFTM). The proposed method leverages the unique features of minutiae points in fingerprints and combines them with other biometric modalities, such as iris and facial recognition, to create a fixed-size template for matching. The fusion process involves extracting and normalizing minutiae points from the fingerprint, followed by their integration with iris and facial features using a robust feature fusion algorithm. The fixed-size template ensures consistency and efficiency in the matching process, addressing challenges related to template size variability and computational overhead. Extensive experiments conducted on standard biometric datasets demonstrate that the proposed MFTM approach significantly enhances authentication accuracy, reduces false acceptance and rejection rates, and provides a highly secure and scalable authentication solution suitable for various applications, including access control and identity verification. The results show an authentication accuracy of 98.7%, a false acceptance rate (FAR) of 0.2%, and a false rejection rate (FRR) of 0.5%. Additionally, the computational time for matching is reduced by 25% compared to traditional methods, highlighting the efficiency and practicality of the proposed approach.
Multi-Fusion Biometric Authentication , , Minutiae-Driven , , Fixed-Size Template Matching (MFTM) , , Fingerprint Recognition , Iris Recognition , , Facial Recognition , Biometric Template , Feature Fusion , Authentication Accuracy
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