Detection and rectification of distorted fingerprints
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The document discusses the issue of elastic distortion in fingerprints, which can lead to false non-match outcomes, particularly in sensitive applications like negative recognition. It introduces algorithms that detect and rectify these distortions using single fingerprint images by employing support vector machines for classification and a reference database for regression. Promising results were achieved on multiple fingerprint databases, indicating the effectiveness of the proposed methods.
Detection and rectification of distorted fingerprints
- 1. DETECTION AND RECTIFICATION OF DISTORTED FINGERPRINTS ABSTRACT Elastic distortion of fingerprints is one of the major causes for false non-match. While this problem affects all fingerprint recognition applications, it is especially dangerous in negative recognition applications, such as watch list and reduplications applications. In such applications, malicious users may purposely distort their fingerprints to evade identification. In this paper, we proposed novel algorithms to detect and rectify skin distortion based on a single fingerprint image. Distortion detection is viewed as a two-class classification problem, for which the registered ridge orientation map and period map of a fingerprint are used as the feature vector and a SVM classifier is trained to perform the classification task. Distortion rectification (or equivalently distortion field estimation) is viewed as a regression problem, where the input is a distorted fingerprint and the output is the distortion field. To solve this problem, a database (called reference database) of various distorted reference fingerprints and corresponding distortion fields is built in the offline stage, and then in the online stage, the nearest neighbor of the input fingerprint is found in the reference database and the corresponding distortion field is used to transform the input fingerprint into a normal one. Promising results have been obtained on three databases containing many distorted fingerprints, namely FVC2004 DB1, Tsinghua Distorted Fingerprint database, and the NIST SD27 latent finer print database.