Computer Vision, Graphics, and Image Processing
On-Line Fingerprint Verification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint Image Enhancement: Algorithm and Performance Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quality Measures of Fingerprint Images
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
IEEE Transactions on Pattern Analysis and Machine Intelligence
EURASIP Journal on Advances in Signal Processing
Singular Points Detection Based on Zero-Pole Model in Fingerprint Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Adaptive fingerprint image enhancement with fingerprint image quality analysis
Image and Vision Computing
Fingerprint quality indices for predicting authentication performance
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
A Comparative Study of Fingerprint Image-Quality Estimation Methods
IEEE Transactions on Information Forensics and Security
Fingerprint-Quality Index Using Gradient Components
IEEE Transactions on Information Forensics and Security
Fingerprint Image-Quality Estimation and its Application to Multialgorithm Verification
IEEE Transactions on Information Forensics and Security
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Automatic assessment of Fingerprint Image Quality (FIQ) has significant influence on the performance of Automated Fingerprint Identification Systems (AFISs). Local texture and global texture clarity of fingerprint images are the main factors in the evaluation of FIQ. Available image size, dryness and Singular Points (SPs) of a fingerprint image are also considered as cofactors, each of them has different effect on the assessment of image quality. In this paper, Homogeneous-Zones-Divide is proposed to evaluate the global clarity of a fingerprint image. To be consistent with human perception of fingerprint images quality, the optimal weight is obtained by a constrained nonlinear optimization model. This optimal weight is further used to assess Composite Quality Score (CQS). Simulation on public database indicates that the precision of our method can achieve 97.5% of accurate rate and it can reasonably classify fingerprint images into four grades, which is helpful to improve the performance of (AFIS).