Fingerprint quality assessment based on texture and geometric features

Ching Han Chen, Chen Shuo An, Ching Yi Chen

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Fingerprint quality assessments are generally used to evaluate the quality of images obtained from fingerprint sensors, and effective fingerprint quality assessment methods are crucial to establishing high-performance biometric identification systems. The use of fingerprint quality assessments helps improve the accuracy of fingerprint registration and user satisfaction. NIST Fingerprint Image Quality (NFIQ) is a popular fingerprint quality assessment algorithm; however, it is unable to provide high-quality assessments for some partial fingerprint images obtained from mobile device sensors. In this study, a hybrid fingerprint assessment framework that integrated texture and geometric features was examined. The final quality assessment values obtained by the framework were higher than those obtained using NFIQ, effectively elevating the performance of existing NFIQ algorithms and expanding its scope of application for different fingerprint images.

Original languageEnglish
Article number040403
JournalJournal of Imaging Science and Technology
Issue number4
StatePublished - Aug 2020


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