Improved fingerprint identification with supervised filtering enhancement.
An important step in the fingerprint identification system is the reliable extraction of distinct features from fingerprint images. Identification performance is directly related to the enhancement of fingerprint images during or after the enrollment phase. Among the various enhancement algorithms, artificial-intelligence-based feature-extraction techniques are attractive owing to their adaptive learning properties. We present a new supervised filtering technique that is based on a dynamic neural-network approach to develop a robust fingerprint enhancement algorithm. For pattern matching, a joint transform correlation (JTC) algorithm has been incorporated that offers high processing speed for real-time applications. Because the fringe-adjusted JTC algorithm has been found to yield a significantly better correlation output compared with alternate JTCs, we used this algorithm for the identification process. Test results are presented to verify the effectiveness of the proposed algorithm.
Bal
Abdullah
A
Department of Electrical and Computer Engineering, University of South Alabama, Mobile, Alabama 36688-0002, USA.
El-Saba
Aed M
AM
Alam
Mohammad S
MS
D000465:Algorithms; D001185:Artificial Intelligence; D001699:Biometry / Q000379:methods*; D003878:Dermatoglyphics; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013223:Statistics as Topic