A fingerprint classification technique using directional images

Abstract

We present a fast, automated, feature-based technique for classifying fingerprints. The technique extracts the singular points (delta and core points) in fingerprints obtained from directional histograms. The technique enhances the digitized image using adaptive clipping and image matching, finds the directional image by checking the orientations of individual pixels, computes directional histograms using overlapping blocks in the directional image, and classifies the fingerprint into the Wirbel class (whorl and twin loop) or the Lasso class (arch, tented arch, right loop, or left loop). The complexity of the technique is on the order of the number of pixels in the fingerprint image. The technique does not require iterations or feedback, and is highly parallel.