Fingerprint Image Quality

Fingerprint image quality assessment is crucial for reliable biometric systems, aiming to objectively measure image suitability for accurate fingerprint matching. Current research focuses on developing robust quality metrics, particularly for contactless fingerprint images acquired via smartphones and other mobile devices, often employing machine learning models like self-supervised dual encoders or adapting existing algorithms (e.g., NFIQ) through retraining on synthetic or diverse datasets. Improved quality assessment is vital for enhancing the accuracy and fairness of fingerprint recognition systems, addressing biases related to demographics and improving overall system performance by enabling informed decision-making, such as image rejection or adaptive fusion strategies.