Watchlist Challenge: 3rd Open-set Face Detection and Identification
This addresses the need for accurate face recognition in uncontrolled settings for biometrics and surveillance, but it is incremental as it focuses on evaluating existing methods with new protocols.
The paper tackled the problem of face detection and open-set identification in real-world surveillance by evaluating algorithms on the enhanced UCCS dataset, finding that detection is robust but open-set identification performance varies, with models pre-trained on large-scale datasets performing better but needing improvement at lower thresholds.
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.