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.
Finally, the work in @cite_7 proposed quality measures related to machine accuracy, and other measures related to human perceived quality. The authors assigned a quality groundtruth to the images from the Labeled Faces in the Wild database according to human perceived quality using the Amazon Mechanical Turk service. The output of the algorithm is a prediction of the machine accuracy and the human perceived quality. They employed a pretrained CNN (VGGFace) for extracting features from the images. Then, they used those features to train an output stage (a SVM). The authors concluded that both measures are correlated to the recognition accuracy, but that human perceived quality is a more accurate predictor.
@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.