Watchlist Challenge: 3rd Open-set Face Detection and Identification
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913497315540992 |
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| author | Kasım, Furkan Boult, Terrance E. Mora, Rensso Biesseck, Bernardo Ribeiro, Rafael Schlueter, Jan Repák, Tomáš Vareto, Rafael Henrique Menotti, David Schwartz, William Robson Günther, Manuel |
| author_facet | Kasım, Furkan Boult, Terrance E. Mora, Rensso Biesseck, Bernardo Ribeiro, Rafael Schlueter, Jan Repák, Tomáš Vareto, Rafael Henrique Menotti, David Schwartz, William Robson Günther, Manuel |
| contents | In the current landscape of biometrics and surveillance, the ability to accurately recognize faces in uncontrolled settings is paramount. The Watchlist Challenge addresses this critical need by focusing on face detection and open-set identification in real-world surveillance scenarios. This paper presents a comprehensive evaluation of participating algorithms, using the enhanced UnConstrained College Students (UCCS) dataset with new evaluation protocols. In total, four participants submitted four face detection and nine open-set face recognition systems. The evaluation demonstrates that while detection capabilities are generally robust, closed-set identification performance varies significantly, with models pre-trained on large-scale datasets showing superior performance. However, open-set scenarios require further improvement, especially at higher true positive identification rates, i.e., lower thresholds. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_07220 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Watchlist Challenge: 3rd Open-set Face Detection and Identification Kasım, Furkan Boult, Terrance E. Mora, Rensso Biesseck, Bernardo Ribeiro, Rafael Schlueter, Jan Repák, Tomáš Vareto, Rafael Henrique Menotti, David Schwartz, William Robson Günther, Manuel Computer Vision and Pattern Recognition In the current landscape of biometrics and surveillance, the ability to accurately recognize faces in uncontrolled settings is paramount. The Watchlist Challenge addresses this critical need by focusing on face detection and open-set identification in real-world surveillance scenarios. This paper presents a comprehensive evaluation of participating algorithms, using the enhanced UnConstrained College Students (UCCS) dataset with new evaluation protocols. In total, four participants submitted four face detection and nine open-set face recognition systems. The evaluation demonstrates that while detection capabilities are generally robust, closed-set identification performance varies significantly, with models pre-trained on large-scale datasets showing superior performance. However, open-set scenarios require further improvement, especially at higher true positive identification rates, i.e., lower thresholds. |
| title | Watchlist Challenge: 3rd Open-set Face Detection and Identification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.07220 |