Wildest Faces: Face Detection and Recognition in Violent Settings
This addresses the challenge of face recognition in unconstrained, violent environments for computer vision researchers, but it is incremental as it primarily introduces a new dataset.
The authors tackled the problem of face detection and recognition in violent settings by introducing the Wildest Faces dataset, which captures adverse effects like low resolution and occlusion, and found that state-of-the-art techniques perform poorly in such scenarios.
With the introduction of large-scale datasets and deep learning models capable of learning complex representations, impressive advances have emerged in face detection and recognition tasks. Despite such advances, existing datasets do not capture the difficulty of face recognition in the wildest scenarios, such as hostile disputes or fights. Furthermore, existing datasets do not represent completely unconstrained cases of low resolution, high blur and large pose/occlusion variances. To this end, we introduce the Wildest Faces dataset, which focuses on such adverse effects through violent scenes. The dataset consists of an extensive set of violent scenes of celebrities from movies. Our experimental results demonstrate that state-of-the-art techniques are not well-suited for violent scenes, and therefore, Wildest Faces is likely to stir further interest in face detection and recognition research.