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.
To date, the work presented in @cite_7 is probably the most advanced approach to face quality estimation. However, it still presents some drawbacks: 1) a high amount of human effort is required to label their database with human perceived quality; and 2) a manual selection of a high quality image is used for each user to obtain the machine accuracy prediction, this also involves human effort and human bias.
@cite_7
2026404762
This issue includes two articles with additional research examples from distinguished experts in social science and computer science. In the first article, "Secondary Avatars and Semiautonomous Agents" presents semiautonomous agent assistants that have been incorporated into recent MMOGs. In the second article, a group of people constructed combat, mentoring, and trust networks based on data from Sony's popular MMOG Everquest II. They also provide an excellent summary of their ongoing research in performance and learning, player churn analysis, and identifying undesirable behavior in MMOGs. The authors argue that the close collaboration between social scientists and computer scientists is creating an emerging area called "computational social science," where computation is used as an integral mechanism in social science research.