Quality assessment of image-based biometric information
The quality of biometric raw data is one of the main factors affecting the overall performance of biometric systems. Poor biometric samples increase the enrollment failure and decrease the system performance. Hence, controlling the quality of the acquired biometric raw data is essential in order to have useful biometric authentication systems. Towards this goal, we present a generic methodology for the quality assessment of image-based biometric modality combining two types of information: 1) image quality and 2) pattern-based quality using the scale-invariant feature transformation (SIFT) descriptor. The associated metric has the advantages of being multimodal (face, fingerprint, and hand veins) and independent from the used authentication system. Six benchmark databases and one biometric verification system are used to illustrate the benefits of the proposed metric. A comparison study with the National Institute of Standards and Technology (NIST) fingerprint image quality (NFIQ) metric proposed by the NIST shows the benefits of the presented metric.
biometrics scale-invariant feature transformation (sift) no-reference image quality assessment support vector machine (svm)