Blind Image Quality Assessment for Face Pose Problem

No-Reference image quality assessment for face images is of high interest since it can be required for biometric systems such as biometric passport applications to increase system performance. This can be achieved by controlling the quality of biometric sample images during enrollment. This paper proposes a novel no-reference image quality assessment method that extracts several image features and uses data mining techniques for detecting the pose variation problem in facial images. Using subsets from three public 2D face databases PUT, ENSIB, and AR, the experimental results recorded a promising accuracy of 97.06% when using the RandomForest Classifier, which outperforms other classifier

Fingerprint Quality Assessment Using a No-Reference Image Quality Metric

International audienceThe quality assessment of the acquired biometric raw data is very important as it deeply affects the performance of biometric systems and consequently their usability. Poor quality samples increase the enrolment failures, and decrease the system performance. In this paper, we present a new quality assessment metric of fingerprints. Its main originality lies in the use of a no-reference image quality metric. The proposed quality metric combines two types of parameters through a weighted sum optimized by a genetic algorithm: 1) image quality criterion and 2) pattern-based quality criteria (salient and patch-based features). BOZORTH3 matching system and the FVC2002 DB3 fingerprint database are used to clarify the benefits of the presented quality metric