Speaker dependent video indexing based on audio-visual interaction

A content-based video indexing method is presented in this paper that aims at temporally indexing a video sequence according to the actual speaker. This is achieved by the integration of audio and visual information. Audio analysis leads to the extraction of a speaker identity label versus time diagram. Visual analysis includes scene cut detection, face shot determination, mouth region extraction and tracking and finally talking face shot determination. Results from both sources are combined to improve speaker-dependent video indexing. Such a task enables flexible video retrieval or browsing in cases where queries according to speaker identities are imposed. Speaker recognition errors are reduced to 2%.