Improving Performance of Facial Biometrics With Quality-Driven Dataset Filtering

Advancements in deep learning techniques and availability of large scale face datasets led to signi cant perfor- mance gains in face recognition in recent years. Modern face recognition algorithms are trained on large- scale in-the-wild face datasets. At the same time, many facial biometric applications rely on controlled image acquisition and enrollment procedures (for instance, document security applications). That is why such face recognition approaches can demonstrate the de ciency of the performance in the target scenario (ICAO-compliant images). However, modern approaches for face image quality estimation may help to mitigate that problem. In this work, we introduce a strategy for ltering training datasets by quality metrics and demonstrate that it can lead to performance improvements in biometric applications that rely on face image modality. We lter the main academic datasets using the proposed ltering strategy and present performance metrics.