Efficient and Accurate Tracking for Face Diarization via Periodical Detection

Face diarization, i.e. face tracking and clustering within video documents, is useful and important for video indexing and fast browsing but it is also a difficult and time consuming task. In this paper, we address the tracking aspect and propose a novel algorithm with two main contributions. First, we propose an approach that leverages state-of-the-art deformable part-based model (DPM) face detector with a multi-cue discriminant tracking-by-detection framework that relies on automatically learned long-term time-interval sensitive association costs specific to each document type. Secondly to improve performance, we propose an explicit false alarm removal step at the track level to efficiently filter out wrong detections (and resulting tracks). Altogether, the method is able to skip frames, i.e. process only 3 to 4 frames per second - thus cutting down computational cost - while performing better than state-of-the-art methods as evaluated on three public benchmarks from different context including a movie and broadcast data.