Outlier-Robust Neural Aggregation Network for Video Face Identification

Current approaches for video face recognition rely on image sets containing faces of exclusively one identity. However, as image sets are created by unsupervised methods, it is necessary to consider outlier-afflicted sets for real-life applications. In this paper, we propose an Outlier-Robust Neural Aggregation Network (ORNAN). First, we embed each image into a feature space using a Convolutional Neural Network (CNN). With the help of two cascaded attention blocks, we predict outliers within the image set. By integrating this knowledge into our aggregation network, we adaptively aggregate all feature vectors to form a single feature, mitigating the influence of outliers and noisy features. We show that our network is robust against outliers using outlier-afflicted IJB-B and IJB-C benchmarks while maintaining similar performance without outliers.