Fingerprint quality assessment using a no-reference image quality metric

The quality assessment of the acquired biometric raw data is very important as it deeply affects the performance of biometric systems and consequently their usability. Poor quality samples increase the enrolment failures, and decrease the system performance. In this paper, we present a new quality assessment metric of fingerprints. Its main originality lies in the use of a no-reference image quality metric. The proposed quality metric combines two types of parameters through a weighted sum optimized by a genetic algorithm: 1) image quality criterion and 2) pattern-based quality criteria (salient and patch-based features). BOZORTH3 matching system and the FVC2002 DB3 fingerprint database are used to clarify the benefits of the presented quality metric.

[1]  Johannes Merkle,et al.  Towards Improving the NIST Fingerprint Image Quality (NFIQ) Algorithm , 2010, BIOSIG.

[2]  Elham Tabassi,et al.  Performance of Biometric Quality Measures , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Christophe Charrier,et al.  DCT statistics model-based blind image quality assessment , 2011, 2011 18th IEEE International Conference on Image Processing.

[4]  LinLin Shen,et al.  Quality Measures of Fingerprint Images , 2001, AVBPA.

[5]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[6]  Josef Kittler,et al.  A multimodal biometric test bed for quality-dependent, cost-sensitive and client-specific score-level fusion algorithms , 2010, Pattern Recognit..

[7]  Xudong Jiang,et al.  Fingerprint quality and validity analysis , 2002, Proceedings. International Conference on Image Processing.

[8]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[9]  Julian Fiérrez,et al.  A Comparative Study of Fingerprint Image-Quality Estimation Methods , 2007, IEEE Transactions on Information Forensics and Security.

[10]  Anil K. Jain,et al.  Fingerprint Image Enhancement: Algorithm and Performance Evaluation , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Anil K. Jain,et al.  FVC2002: Second Fingerprint Verification Competition , 2002, Object recognition supported by user interaction for service robots.

[12]  Haiyun Xu,et al.  Gabor filters as candidate quality measure for NFIQ 2.0 , 2012, 2012 5th IAPR International Conference on Biometrics (ICB).

[13]  Chulhan Lee,et al.  Model-Based Quality Estimation of Fingerprint Images , 2006, ICB.

[14]  Josef Kittler,et al.  Quality-Based Score Normalization With Device Qualitative Information for Multimodal Biometric Fusion , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[15]  Charles L. Wilson,et al.  A novel approach to fingerprint image quality , 2005, IEEE International Conference on Image Processing 2005.

[16]  Anil K. Jain,et al.  Fingerprint Quality Indices for Predicting Authentication Performance , 2005, AVBPA.

[17]  Yunhong Wang,et al.  Asymmetry-Based Quality Assessment of Face Images , 2009, ISVC.

[18]  Tom V. Mathew.  Genetic Algorithm , 2022 .