Learned templates for feature extraction in fingerprint images
Most current techniques for minutiae extraction in fingerprint images utilize complex preprocessing and postprocessing. In this paper, we propose a new technique, based on the use of learned templates, which statistically characterize the minutiae. Templates are teamed from examples by optimizing a criterion function using Lagrange's method. To detect the presence of minutiae in test images, templates are applied with appropriate orientations to the binary image only at selected potential minutia locations. Several performance measures, which evaluate the quality and quantity of extracted features and their impact on identification, are used to evaluate the significance of learned templates. The performance of the proposed approach is evaluated on two sets of fingerprint images: one is collected by an optical scanner and the other one is chosen from NIST special fingerprint database 4. The experimental results show that learned templates can improve both the features and the performance of the identification system.