Improving Performance of Facial Biometrics With Quality-Driven Dataset Filtering

Advancements in deep learning techniques and availability of large scale face datasets led to significant performance 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 deficiency 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 filtering training datasets by quality metrics and demonstrate that it can lead to performance improvements in biometric applications that rely on face image modality. We filter the main academic datasets using the proposed filtering strategy and present performance metrics.