An exploratory study to identify relevant cues for the deletion of faces for multimedia retrieval

Within our approach to big data, we reduce the number of images in video footage by applying a shot detection with a keyframe extraction of single frames. This can be followed by duplicate removal and face detection processes yielding to a further data reduction. Nevertheless, additional reductions steps are necessary in order to make the data manageable (searchable) for the end user in a meaningful way. Therefore, we investigated human inspired forgetting as a data reduction tool. We conducted an exploratory study on a subset of the remaining face data to examine patterns in the selection process of faces that are considered most memorable showing a potential of roughly above 75 % for elimination. The results of the study considered the quality and the size of the faces as important measures. In these terms, we finally show a connection to characteristics of state-of-the-art face detectors.