Enhancement of feature extraction for low-quality fingerprint images using stochastic resonance

摘要

This paper presents a new approach to enhancing feature extraction for low-quality fingerprint images by adding noise to the original signal. Feature extraction often fails for low-quality fingerprint images obtained from excessively dry or wet fingers. In nonlinear signal processing systems, a moderate amount of noise can help amplify a faint signal while excessive amounts of noise can degrade the signal. Stochas-tic resonance (SR) refers to a phenomenon where an appropriate amount of noise added to the original signal can increase the signal-to-noise ratio. Experimental results show that Gaussian noise added to low-quality fingerprint images enables the extraction of useful features for biometric identification. SR was applied to 20 fingerprint images in the FVC2004 DB2 database that were rejected by a state-of-the-art fingerprint verification algorithm due to failures in feature extraction. SR enabled feature extraction from 10 out of 11 low-quality images with poor contrast. The remaining nine images were damaged finger-prints from which no meaningful features can be obtained. Improved feature extraction using SR decreases an equal error rate of fingerprint verification from 6.55% to 5.03%. The receiver operating char-acteristic curve shows that the genuine acceptance rates are improved for all false acceptance rates.