One of the big issues in biometric recognition is robustness of recognition accuracy against sample signal quality degradation. The performance of a biometric recognition system is usually heavily affected by sample signal quality. A wide variety of factors potentially influence the quality of acquired biometric samples. The different types of features that can be extracted from biometric samples influence the impact of quality degradations on recognition performance in various ways. Moreover, there is interplay among different types of feature extraction and acquisition technology/conditions such that it is not clear a priori which type of feature extraction is favourable under which conditions. Therefore, it is essential to provide reliable methodology to comparatively assess biometric recognition robustness under varying conditions.

Chapter Contents:

• 9.1 Introduction
• 9.2 Finger-vein recognition and datasets
• 9.2.1 Finger-vein recognition algorithms
• 9.2.2 Datasets
• 9.3 StirMark distortion robustness: modelling acquisition conditions
• 9.3.1 Results
• 9.4 Compression robustness
• 9.4.1 Results
• 9.5 Sensor ageing robustness
• 9.5.1 Results
• 9.6 Conclusion
• Acknowledgements
• References

Inspec keywords: feature extraction; vein recognition; fingerprint identification

Other keywords: finger-vein recognition; recognition performance; biometric recognition robustness assessment; biometric recognition system; feature extraction; sample signal quality; sample signal quality degradation

Subjects: Computer vision and image processing techniques; Image recognition; Data security