title: A support vector machine approach for truncated fingerprint image detection from sweeping fingerprint sensors.
abstract: A sweeping fingerprint sensor converts fingerprints on a row by row basis through image reconstruction techniques. However, a built fingerprint image might appear to be truncated and distorted when the finger was swept across a fingerprint sensor at a non-linear speed. If the truncated fingerprint images were enrolled as reference targets and collected by any automated fingerprint identification system (AFIS), successful prediction rates for fingerprint matching applications would be decreased significantly. In this paper, a novel and effective methodology with low time computational complexity was developed for detecting truncated fingerprints in a real time manner. Several filtering rules were implemented to validate existences of truncated fingerprints. In addition, a machine learning method of supported vector machine (SVM), based on the principle of structural risk minimization, was applied to reject pseudo truncated fingerprints containing similar characteristics of truncated ones. The experimental result has shown that an accuracy rate of 90.7% was achieved by successfully identifying truncated fingerprint images from testing images before AFIS enrollment procedures. The proposed effective and efficient methodology can be extensively applied to all existing fingerprint matching systems as a preliminary quality control prior to construction of fingerprint templates.
authors.affiliation: Department of Computer Science and Engineering, National Taiwan Ocean University, Pei-Ning Road, Keelung 20224, Taiwan. jc60516@gmail.com.
authors.forename: Chi-Jim
authors.initials: CJ
authors.lastname: Chen
authors.affiliation: Department of Computer Science and Engineering, National Taiwan Ocean University, Pei-Ning Road, Keelung 20224, Taiwan. twp@mail.ntou.edu.tw.
authors.forename: Tun-Wen
authors.initials: TW
authors.lastname: Pai
authors.affiliation: Egis Technology Inc., 2F, No. 360, Rueiguang Road, Neihu District, Taipei 11492, Taiwan. mox.cheng@egistec.com.
authors.forename: Mox
authors.initials: M
authors.lastname: Cheng
mesh_terms: D001185:Artificial Intelligence; D001699:Biometry / Q000295:instrumentation; D015374:Biosensing Techniques / Q000295:instrumentation; D003878:Dermatoglyphics; D006801:Humans; D010363:Pattern Recognition, Automated; D060388:Support Vector Machine
references.citation: IEEE Trans Pattern Anal Mach Intell. 2012 Mar;34(3):451-64
references.pmid: 21808092