  Wearable cameras offer a hands-free way to record egocentric images of daily
experiences, where social events are of special interest. The first step
towards detection of social events is to track the appearance of multiple
persons involved in it. In this paper, we propose a novel method to find
correspondences of multiple faces in low temporal resolution egocentric videos
acquired through a wearable camera. This kind of photo-stream imposes
additional challenges to the multi-tracking problem with respect to
conventional videos. Due to the free motion of the camera and to its low
temporal resolution, abrupt changes in the field of view, in illumination
condition and in the target location are highly frequent. To overcome such
difficulties, we propose a multi-face tracking method that generates a set of
tracklets through finding correspondences along the whole sequence for each
detected face and takes advantage of the tracklets redundancy to deal with
unreliable ones. Similar tracklets are grouped into the so called extended
bag-of-tracklets (eBoT), which is aimed to correspond to a specific person.
Finally, a prototype tracklet is extracted for each eBoT, where the occurred
occlusions are estimated by relying on a new measure of confidence. We
validated our approach over an extensive dataset of egocentric photo-streams
and compared it to state of the art methods, demonstrating its effectiveness
and robustness.
