Pattern recognition and image analysis
Pattern recognition and image analysis
Fingerprint Image Enhancement: Algorithm and Performance Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing: PIKS Inside
Digital Image Processing: PIKS Inside
Digital Image Processing
Quality Measures of Fingerprint Images
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
FVC2002: Second Fingerprint Verification Competition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Minutiae-based partial fingerprint recognition
Minutiae-based partial fingerprint recognition
Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
Advanced feature extraction algorithms for automatic fingerprint recognition systems
Advanced feature extraction algorithms for automatic fingerprint recognition systems
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
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This paper presents a novel technique that employs a hierarchical k-means clustering for quality based classification of fingerprints for subsequent improvement in fingerprint matching results. A set of statistical and frequency features have been calculated from a fingerprint image. A hierarchical k-means clustering algorithm has been utilized to classify the fingerprint image into one of four quality classes, i.e. good, dry, normal or wet. An objective method has also been proposed to evaluate the performance of fingerprint quality classification. It has been shown through experimental results that the performance of minutiae based matcher improves when the quality of fingerprint image is incorporated in the matching stage. The false accept rate and false reject rate of minutiae based fingerprint matcher are 1.8 on FVC 2002 db1 database without utilizing fingerprint quality information. False accept rate has been reduced from 1.8 to 0.79 whereas the false reject rate is at 1.8 when fingerprint quality based threshold value is utilized. This significant improvement in the performance of the fingerprint matching system shows the effectiveness of hierarchical k-means clustering technique in quality based classification of fingerprints.