Quality Beyond Perception: Introducing Image Quality Metrics for Enhanced Facial and Fingerprint Recognition

Abstract

Assessing the quality of biometric images is key to making recognition technologies more accurate and reliable. Our research began with fingerprint recognition systems and later expanded to facial recognition systems, underscoring the importance of image quality in both areas. For fingerprint recognition, image quality is vital for accuracy. We developed the Fingerprint RecognitionBased Quality (FRBQ) metric, which improves on the limitations of the NFIQ2 model. FRBQ leverages deep learning algorithms in a weakly supervised setting, using matching scores from DeepPrint, a FixedLength Fingerprint Representation Model. Each score is labeled to reflect the robustness of fingerprint image matches, providing a comprehensive metric that captures diverse perspectives on image quality. Comparative analysis with NFIQ2 reveals that FRBQ correlates more strongly with recognition scores and performs better in evaluating challenging fingerprint images. Tested with the FVC 2004 dataset, FRBQ has proven effective in assessing fingerprint image quality. After our success with fingerprint recognition, we turned to facial recognition systems. In facial recognition, image quality involves more than just perceptual aspects; it includes features that convey identity information. Existing datasets consider factors like illumination and pose, which enhance robustness and performance. However, age variations and emotional expressions can still pose challenges. To tackle these, we introduced the Unified Tri-Feature Quality Metric (U3FQ). This framework combines age variance, facial expression similarity, and congruence scores from advanced recognition models like VGG-Face, ArcFace, FaceNet, and OpenFace. U3FQ uses a Regression Network model specifically designed for facial image quality assessment. We compared U3FQ to general image quality assessment techniques like BRISQUE, BLINDS-II, and RankIQA, as well as specialized facial image quality methodologies like PFE, SER-FIQA, and SDD-FIQA. Our results, supported by analyses such as DET plots, expression match heat maps, and EVRC curves, show U3FQ’s effectiveness. Our study highlights the transformative potential of artificial intelligence in biometrics, capturing critical details that traditional methods might miss. By providing precise quality assessments, we emphasize its role in advancing both fingerprint and facial recognition systems. This work sets the stage for further research and innovation in biometric analysis, underlining the importance of image quality in improving recognition technologies.