Freq-HD: An Interpretable Frequency-based High-Dynamics Affective Clip Selection Method for in-the-Wild Facial Expression Recognition in Videos

The in-the-wild dynamic facial expression recognition (DFER) has been challenging due to several high-dynamics factors such as limited dynamic expression-related frames and variable non-expression noise in facial expression sequences. To provide more expression-related clips for DFER models, we propose a novel and interpretable frequency-based method (Freq-HD) for high-dynamics affective clip selection. It can select clips containing pure expression changes from sequences and aid different DFER network structures in recognizing in-the-wild dynamic facial expressions more accurately and efficiently. We first design a novel spatial-temporal frequency analysis (STFA) module to compute the dynamics values of each clip by using sliding windows and spatial-temporal frequency analysis. Moreover, we propose a multi-band complementary selection (MBC) module to amend the inappropriate reaction of the dynamics values of different spatial frequency bands in STFA when expression-irrelevant noise occurs. Specifically, the MBC uses an ingenious mapping method to generate the inhibitory factors to complement and separate the dynamics of expressions and non-expressions in different frequency bands. The Freq-HD can select the most expression-correlated clips and the consisting frames, which could be incorporated into any existing DFER models. We extensively evaluate the Freq-HD on two in-the-wild datasets and four DFER baselines, showing that our method significantly improves the subsequent network performance while using fewer input frames and reducing computation cost. More ablation studies and visualization analysis provide further empirical evidence of the effectiveness of our method.