  The contemporary state-of-the-art of Dynamic Facial Expression Recognition
(DFER) technology facilitates remarkable progress by deriving emotional
mappings of facial expressions from video content, underpinned by training on
voluminous datasets. Yet, the DFER datasets encompass a substantial volume of
noise data. Noise arises from low-quality captures that defy logical labeling,
and instances that suffer from mislabeling due to annotation bias, engendering
two principal types of uncertainty: the uncertainty regarding data usability
and the uncertainty concerning label reliability. Addressing the two types of
uncertainty, we have meticulously crafted a two-stage framework aiming at
\textbf{S}eeking \textbf{C}ertain data \textbf{I}n extensive \textbf{U}ncertain
data (SCIU). This initiative aims to purge the DFER datasets of these
uncertainties, thereby ensuring that only clean, verified data is employed in
training processes. To mitigate the issue of low-quality samples, we introduce
the Coarse-Grained Pruning (CGP) stage, which assesses sample weights and
prunes those deemed unusable due to their low weight. For samples with
incorrect annotations, the Fine-Grained Correction (FGC) stage evaluates
prediction stability to rectify mislabeled data. Moreover, SCIU is conceived as
a universally compatible, plug-and-play framework, tailored to integrate
seamlessly with prevailing DFER methodologies. Rigorous experiments across
prevalent DFER datasets and against numerous benchmark methods substantiates
SCIU's capacity to markedly elevate performance metrics.
