# A real-time and unsupervised face Re-Identification system for Human-Robot Interaction

arXiv: https://arxiv.org/abs/1804.03547 · HTML: https://mlsanity.com/paper/1804.03547/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2018.

## Built on by (most cited first, 6 of 6)

- [Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends](https://mlsanity.com/paper/2202.13121/) (2022)
- [SD-ReID: View-aware Stable Diffusion for Aerial-Ground Person Re-Identification](https://mlsanity.com/paper/2504.09549/) (2025)
- [Data Pruning Can Do More: A Comprehensive Data Pruning Approach for Object Re-identification](https://mlsanity.com/paper/2412.10091/) (2024)
- [CARPE-ID: Continuously Adaptable Re-identification for Personalized Robot Assistance](https://mlsanity.com/paper/2310.19413/) (2023)
- [Adapting the re-ID challenge for static sensors](https://mlsanity.com/paper/2412.00290/) (2024)
- [Face versus Body Tracking for Human-Robot Interaction: An Egocentric Dataset](https://mlsanity.com/paper/2606.03694/) (2026)

## Abstract (arXiv)

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.