Finding people in archive films through tracking

The goal of this work is to find all people in archive films. Challenges include low image quality, motion blur, partial occlusion, non-standard poses and crowded scenes. We base our approach on face detection and take a tracking/temporal approach to detection. Our tracker operates in two modes, following face detections whenever possible, switching to low-level tracking if face detection fails. With temporal correspondences established by tracking, we formulate detection as an inference problem in one-dimensional chains/tracks. We use a conditional random field model to integrate information across frames and to re-score tentative detections in tracks. Quantitative evaluations on full-length films show that the CRF-based temporal detector greatly improves face detection, increasing precision for about 30% (suppressing isolated false positives) and at the same time boosting recall for over 10% (recovering difficult cases where face detectors fail).