  Speaker diarization is one of the critical components of computational media
intelligence as it enables a character-level analysis of story portrayals and
media content understanding. Automated audio-based speaker diarization of
entertainment media poses challenges due to the diverse acoustic conditions
present in media content, be it background music, overlapping speakers, or
sound effects. At the same time, speaking faces in the visual modality provide
complementary information and not prone to the errors seen in the audio
modality. In this paper, we address the problem of speaker diarization in TV
shows using the active speaker faces. We perform face clustering on the active
speaker faces and show superior speaker diarization performance compared to the
state-of-the-art audio-based diarization methods. We additionally report a
systematic analysis of the impact of active speaker face detection quality on
the diarization performance. We also observe that a moderately well-performing
active speaker system could outperform the audio-based diarization systems.
