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Video-based face screening is essentially a detection problem where faces captured in video sequences are matched against the facial models of individuals of interest. This problem is associated with several operational challenges, from lighting and pose changes, to natural aging of target individuals, and to the limited availability of reference samples from changing environments to design facial models. Some matchers proposed in literature may be employed to adapt facial models of individuals enrolled to the system in response to new reference samples. This paper reviews and compares the performance of these matchers, focusing on their ability for adapting to new data. An experimental methodology is proposed to assess their performance for video surveillance applications. This methodology is focused on transactional and subject-based performance, and considers the imbalance of positive and negative samples. Experiments are then performed with the Canegie Mellon University Face in Action video dataset, according to matching accuracy and resource requirements. Results indicate that ensemble-based matchers outperform traditional monolithic approaches, maintaining a higher level of accuracy over time when adapting to new reference samples.