Towards Large-scale Unsupervised Face Recognition in Videos
Video content is ubiquitous in the modern world and there is a growing need for automated methods to extract information from videos. Face-based person retrieval is a particularly interesting task in this domain which involves the use of face recognition to track the appearances of people in video data. The availability of good quality datasets is a prerequisite for any deep learning-based face recognition system. Most face datasets are based on web images of celebrities and do not represent the challenges of video face recognition. This research is focused on the development of a large-scale dataset of face images extracted from videos, in order to renew interest in and promote the development of face representation and identification models capable of large-scale, unsupervised face recognition in videos. This project proposed a hierarchical retrieval index for online clustering in order to demonstrate the effectiveness of the proposed dataset in evaluating real-time person retrieval systems. The dataset is also suitable for the evaluation of feature representation and identity classification models on the tasks of face verification, identification, and clustering.