We present a novel measure of fingerprint image quality, which can be used to estimate fingerprint match performance. This means presenting the matcher with good quality fingerprint images will result in high matcher performance, and vice versa, the matcher will perform poorly for poor quality fingerprints. We discuss the implementation of our fingerprint image quality metric and we present the results of testing it on 280 different combinations of fingerprint image data and fingerprint matcher system. We found that the metric predicts matcher performance for all systems and datasets. Our definition of quality can be applied to other biometric modalities and upon proper feature extraction can be used to assess quality of any mode of biometric samples.
Proceedings Title: ICIP 2005, The International Conference on Image Processing
Conference Dates: September 11-14, 2005
Conference Location: genoa,
Pub Type: Conferences
biometrics, fingerprint, image quality, neural network