In this paper we develop a Quality Assessment approach for face recognition based on deep learning. The method consists of a Convolutional Neural Network, FaceQnet, that is used to predict the suitability of a specific input image for face recognition purposes. The training of FaceQnet is done using the VGGFace2 database. We employ the BioLab-ICAO framework for labeling the VGGFace2 images with quality information related to their ICAO compliance level. The groundtruth quality labels are obtained using FaceNet to generate comparison scores. We employ the groundtruth data to fine-tune a ResNet-based CNN, making it capable of returning a numerical quality measure for each input image. Finally, we verify if the FaceQnet scores are suitable to predict the expected performance when employing a specific image for face recognition with a COTS face recognition system. Several conclusions can be drawn from this work, most notably: 1) we managed to employ an existing ICAO compliance framework and a pretrained CNN to automatically label data with quality information, 2) we trained FaceQnet for quality estimation by fine-tuning a pre-trained face recognition network (ResNet-50), and 3) we have shown that the predictions from FaceQnet are highly correlated with the face recognition accuracy of a state-of-the-art commercial system not used during development. FaceQnet is publicly available in GitHub.
Face images to be included into machine readable travel documents have to fulfill quality requirements defined by international ISO standards. The concept of quality in this context extends the common idea of image quality: usually a bad quality image presents visual defects such as blurring or noise while, according to ISO ICAO standard, other factors could make a given sample a poor quality image (e.g., presence of dark glasses or mouth open). The verification of face image conformance to ISO ICAO standards is carried out mostly by humans today, through visual inspection, since a totally automatic evaluation is still not satisfactory. The objective of this work is to present the BioLab-ICAO framework, an evaluation benchmark which will be made available to the scientific community, designed to encourage the research on this topic; it consists of a large ground truth database, a well-defined testing protocol, and baseline algorithms for image compliance verification.