Improved Hierarchical Clustering for Face Images in Videos: Integrating positional and temporal information with HAC

Efficient techniques for face clustering, along with speech and text recognition, can provide the means for rapid browsing and accurate retrieval of videos from large video databases. Observing that video data contains information about not only the face features, but also the temporal ordering of the faces and positional coordinates of the face-regions within the video frame, an attempt is made to consolidate all of these details into the framework of the flexible and intuitive hierarchical clustering algorithm. This paper outlines a novel initialization mechanism for the hierarchical clustering to follow, based on the temporal and positional information of the face-samples extracted from a video. Experiments with news broadcast videos show that the novel algorithm is considerably more efficient than it's parent, and is promising for future exploration.