Improved weak labels using contextual cues for person identification in videos

Fully automatic person identification in TV series has been achieved by obtaining weak labels from subtitles and transcripts [11]. In this paper, we revisit the problem of matching subtitles with face tracks to obtain more assignments and more accurate weak labels. We perform a detailed analysis of the state-of-the-art showing the types of errors during the assignment and providing insights into their cause. We then propose to model the problem of assigning names to face tracks as a joint optimization problem. Using negative constraints between co-occurring pairs of tracks and positive constraints from track threads, we are able to significantly improve the speaker assignment performance. This directly influences the identification performance on all face tracks. We also propose a new feature to determine whether a tracked face is speaking and show further improvements in performance while being computationally more efficient.