Advanced Feature Extraction Algorithms for Automatic Fingerprint Recognition Systems

In this thesis we have developed an improved framework for advanced feature detection algorithms in automatic fingerprint recognition systems. We have studied the factors relating to obtaining high performance feature points detection algorithm, such as image quality, segmentation, image enhancement, feature detection, feature verification and filtering. Fingerprint image quality is an important factor in the performance of automatic fingerprint identification systems(AFIS). Commonly used features for fingerprint image quality are Fourier spectrum energy, Gabor filter energy, local orientation certainty level. However, no systematic method to combine texture features in the frequency domain and spatial domain has been reported in the literature. We propose composite metrics which combine band-limited ring-wedge spectral metrics in the frequency domain and complementary local statistical texture features (called inhomogeneity) in the spatial domain to classify images based on quality and select appropriate preprocessing and enhancement parameters. Accurate segmentation of fingerprint ridges from noisy background are necessary for efficiency and accuracy of subsequent enhancement and feature extraction algorithms.