Attribute-Aware Amplification of Facial Feature Sequences for Facial Emotion Recognition

Many works have been proposed to automatically recognize facial human emotions, yet distinguishing subtle emotions still remains a challenge. However, just amplifying these facial movements as a whole does not accurately reflect the actual expressions as action intensity is different for each facial part. We propose an attributionaware amplification of facial descriptors that considers gender, facial components, and emotions to alleviate this issue. The amplifications are also continuously updated in an evolutionary manner during training using Population Based Augmentation (PBA). We found that applying different amplifications for each facial region/unit is crucial for the success of our method, outperforming the competing approaches on both the RAVDESS and THAI-SER datasets.