Abstract
Through the analysis of biological, behavioral, or a combination of both traits, biometrics entails the identification of specific persons. Finger veins, iris patterns, fingerprints, and DNA are examples of common biometric qualities. Of these, fingerprints are the most commonly utilized since they are highly distinctive, simple to obtain, and can be obtained from a variety of sources (because each person has ten fingers). In addition to introducing a method for identifying the region of interest (ROI), which is a specific area selected from the fingerprint image to improve feature extraction, this research focuses on several models for extracting fingerprint features. The study suggests a novel technique known as the flexible region of interest (FROI) for extracting a wider region of interest. Using the GLCM algorithm and invariant moments, features were retrieved from four fingerprint models (gray, binary, ridges thinning, and valleys thinning) using this FROI. The highest performance was obtained with invariant moments recovered from the valleys thinning model, according to experimental results from a verification system. This led to a false rejection rate (FRR) of 7.5% and a false acceptance rate (FAR) of 0.3846%
Published Version
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