Fingerprint recognition is a significant aspect of biometric authentication systems, recognized for its reliability and uniqueness. It introduces an innovative technology for enhanced fingerprint recognition implemented through the use of the Speeded-Up Robust Features (SURF) algorithm widely acknowledged to provide the best-performing approach to pattern detection and matching. This paper aims to explain the role of the SURF algorithm for fingerprint recognition, compare it with the conventional minutia-based model, and evaluate the effectiveness of these two methods. Simulations are carried out to compare the precision, and speed of both approaches under different circumstances. The findings reveal that the proposed SURF-based system is faster and more resistant to noise compared to the minutiae-based approach. The findings are used to design better biometric systems to reduce risks and improve finger print-based means of identification.