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, con-trolling 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 biomet-ric modality combining two types of information: 1) image quality and 2) pattern-based quality using the 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 Preprint submitted to Elsevier December 20, 2014 the proposed metric. A comparison study with the NFIQ metric proposed by the NIST shows the benefits of the presented metric.