Ruud M. Bolle

Minutia verification and classification for fingerprint matching

We propose a feedback path for the feature extraction stage, followed by a feature refinement stage for improving the matching performance. This performance improvement is illustrated in the context of a minutia-based fingerprint verification system. We show that a minutia verification stage based on re-examining the gray-scale profile in a detected minutias spatial neighborhood in the sensed image can improve the matching performance by /spl sim/4% on our database. Further, we show that a feature refinement stage which assigns a class label to each detected minutia (ridge ending and ridge bifurcation) before matching can also improve the matching performance by /spl sim/3%. A combination of feedback (minutia verification) in the feature extraction phase and feature refinement (minutia classification) improves the overall performance of the fingerprint verification system by /spl sim/8%.

Fingerprint Classification by Decision Fusion

Fingerprint classification is an important indexing method for any large-scale fingerprint recognition system or database, as a method for reducing the number of fingerprints that need to be searched when looking for a matching print. Fingerprints are generally classified into broad categories based on global characteristics. This paper describes novel methods of classification using hidden Markov models (HMMs) and decision trees to recognize the ridge structure of the print, without needing to detect singular points. The methods are compared and combined with a standard fingerprint classification algorithm, and results for the combination are presented using a standard database of fingerprint images. The paper also describes a method for achieving any level of accuracy required of the system, by sacrificing the efficiency of the classifier. The accuracy of the combination classifier is shown to be higher than that of two state-of-the-art systems tested under the same conditions.

Dynamic Behavior in Fingerprint Videos

Traditional fingerprint acquisition is limited to a single image capture and processing. With the advent of faster capture hardware, faster processors, and advances in video compression standards, newer systems capture and exploit video signals for tasks that are difficult using single images. In this chapter, we propose the use of fingerprint video sequences to investigate dynamic behaviors of fingerprints across multiple frames. In particular, we present a novel approach to detect and estimate distortion occurring in compressed fingerprint video streams. Our approach directly works on MPEG-{1,2} encoded fingerprint video bitstreams to estimate interfield flow without decompression and uses this flow information to investigate temporal characteristics of the behaviors of the fingerprints. The joint temporal and motion analysis leads to a novel technique to detect and characterize distortion reliably. The proposed method has been tested on the NIST-24 database, and the results are very promis ing. We also describe a new concept called the “resultant biometrics”—a new type of biometrics that has both a physiological, physical (e.g., force, torque, linear motion, rotation) component and/or temporal characteristic, added by a subject to an existing biometrics. This resultant biometric is both desirable and efficient in terms of easy modification of compromised biometrics and is harder to produce with spoof body parts.