Abstract
Face re-identification is largely thought of as a solved problem in the research community, with State-of-the-art systems attaining human-level performance on unconstrained image datasets. However, these results do not seem to translate to the real world. In systems matching people in surveillance-like footage to high-quality images, reported performance is much lower than what the literature would suggest. In this work, we contribute a multi-modal dataset for evaluating real-world performance of facial re-identification systems. We then perform a verification and re-identification evaluation for state-of-art systems on both this dataset and the popular benchmarking dataset Labelled Faces in the Wild (LFW).
| Original language | English |
|---|---|
| Title of host publication | Irish Machine Vision and Image Processing Conference Proceedings 2018: Proceedings |
| Publisher | Irish Pattern Recognition & Classification Society |
| Pages | 137-144 |
| ISBN (Electronic) | 978-0-9934207-3-3 |
| Publication status | Published - 31 Aug 2018 |
| Event | Irish Machine Vision and Image Processing Conference - Ulster University, Belfast, United Kingdom Duration: 29 Aug 2018 → 31 Aug 2018 Conference number: 2018 |
Conference
| Conference | Irish Machine Vision and Image Processing Conference |
|---|---|
| Abbreviated title | IMVIP |
| Country | United Kingdom |
| City | Belfast |
| Period | 29/08/2018 → 31/08/2018 |
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Cite this
Brown, G., Martinez del Rincon, J., & Miller, P. (2018). A Comparative Study of Face Re-identification Systems under Real-World Conditions. In Irish Machine Vision and Image Processing Conference Proceedings 2018: Proceedings (pp. 137-144). Irish Pattern Recognition & Classification Society. https://iprcs.scss.tcd.ie/pdf/IMVIP2018Book.pdf#page=147