Design of Fuzzy Logic-Based Footprint Authentication to Enhance Global Security

This paper presents a novel method for categorizing footprint images using fuzzy logic, addressing various global security challenges. Biometric footprint authentication is gaining attention due to the unique, permanent, and measurable characteristics of footprints, which have numerous line features. The study discusses feature extraction and identification techniques based on fuzzy logic, utilizing publicly available databases. The method applies a sub-image-based principal line feature extraction technique to low-resolution footprint images. By dividing the image into sub-images, features are combined into a single feature vector, which is input into a fuzzy inference system. Testing was conducted on images collected at RUNGTA COLLEGE BHILAI, aiming to balance trade-offs in biometric systems and reduce error rates to approximately ±1%. The findings suggest that the approach can effectively enhance the reliability and accuracy of biometric systems.