Multi-pose Face Recognition for Person Retrieval in Camera Networks

In this paper, we study the use of facial appearance features for the re-identification of persons using distributed camera networks in a realistic surveillance scenario. In contrast to features commonly used for person reidentification, such as whole body appearance, facial features offer the advantage of remaining stable over much larger intervals of time. The challenge in using faces for such applications, apart from low captured face resolutions, is that their appearance across camera sightings is largely influenced by lighting and viewing pose. Here, a number of techniques to address these problems are presented and evaluated on a database of surveillance-type recordings. A system for online capture and interactive retrieval is presented that allows to search for sightings of particular persons in the video database. Evaluation results are presented on surveillance data recorded with four cameras over several days. A mean average precision of 0.60 was achieved for inter-camera retrieval using just a single track as query set, and up to 0.86 after relevance feedback by an operator.