Detection and Rectification of Distorted Fingerprints

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 37; no. 3; pp. 555 - 568
• Main Authors: Si, Xuanbin, Feng, Jianjiang, Zhou, Jie, Luo, Yuxuan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2015
• Subjects: Biometric Identification - methods; Databases, Factual; Dermatoglyphics - classification; Feature extraction; Fingerprint recognition; Force; Humans; Image Processing, Computer-Assisted - methods; Principal Component Analysis; Skin; Support Vector Machine; Training; Vectors; nearest neighbor regression; Fingerprint; registration; distortion; PCA
• ISSN: 0162-8828; 1939-3539; 2160-9292
• DOI: 10.1109/TPAMI.2014.2345403

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 watchlist and deduplication 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 fingerprint database.