  In the context of Human-Robot Interaction (HRI), face Re-Identification (face
Re-ID) aims to verify if certain detected faces have already been observed by
robots. The ability of distinguishing between different users is crucial in
social robots as it will enable the robot to tailor the interaction strategy
toward the users' individual preferences. So far face recognition research has
achieved great success, however little attention has been paid to the realistic
applications of Face Re-ID in social robots. In this paper, we present an
effective and unsupervised face Re-ID system which simultaneously re-identifies
multiple faces for HRI. This Re-ID system employs Deep Convolutional Neural
Networks to extract features, and an online clustering algorithm to determine
the face's ID. Its performance is evaluated on two datasets: the TERESA video
dataset collected by the TERESA robot, and the YouTube Face Dataset (YTF
Dataset). We demonstrate that the optimised combination of techniques achieves
an overall 93.55% accuracy on TERESA dataset and an overall 90.41% accuracy on
YTF dataset. We have implemented the proposed method into a software module in
the HCI^2 Framework for it to be further integrated into the TERESA robot, and
has achieved real-time performance at 10~26 Frames per second.
