Ordered Weighted Aggregation Networks for Video Face Recognition

Abstract Video face recognition generally includes a step where all descriptors extracted for each frame are aggregated to generate a single video face representation. The most commonly used operator for aggregation is average, which gives the same relevance to each frame. Some adaptive aggregation algorithms have been developed, but most of them rely on the use of weighted mean as aggregation operator, thus disregarding many other types of aggregation operators. In this paper, we propose a novel adaptive aggregation scheme based on ordered weighted average (OWA) operators in contrast with the mainly used weighted mean scheme. Furthermore, besides presenting the theoretical aspects of our aggregation scheme, we develop two different concrete implementations to validate its suitability for video face recognition: Ordered weighted aggregation network (OWANet) and Weighted OWANet (WOWANet). Both algorithms are based on neural networks and are trainable through gradient descent in a classic supervised learning way. We conduct extensive experiments on YouTube Faces, COX Face and the IARPA Janus Benchmark A for evaluating recognition performance on verification and identification tasks. The experimentation process shows that both proposals achieve very competitive results in accuracy with respect to the existent state-of-the-art methods, while significantly reducing space and inference time.