Leveraging Active and Continual Learning for Improving Deep Face Recognition in-the-Wild

Face recognition systems play a vital role in various applications by providing identification and verification based on facial features. However, these systems face challenges in large-scale in-the-wild applications, where the current static pipelines are usually unable to cope with the high velocity, variety, and volume of the data, negatively affecting their accuracy and reliability. To overcome these challenges, in this paper, we propose departing from traditional static face recognition pipelines and moving towards dynamic and adaptable approaches. To this end, we propose a combination of an active and continual learning approach that can automatically augment the model with informative samples gathered during the inference, provided that the model is already confident enough, significantly improving its recognition accuracy. Furthermore, the proposed pipeline also natively incorporates active learning, allowing for using human feedback, when available, to further improve its performance. The effectiveness of the proposed method is validated on a challenging setup using two large-scale face recognition datasets.