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Sequence-level affective level estimation based on pyramidal facial expression features
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DOI：10.1016/j.patcog.2023.109958.png)
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Abstract
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People tend to focus on changes in a certain complex human affect in the majority of practical applications of affective computing. Facial expression classification models are unable to represent all human affects through a limited number of expression categories. In this backdrop, this paper studies the Sequence -level affective level estimation (S -ALE), which is more relevant to real scenarios and can depict individual affective level in continuous manner. A spatio-temporal framework applied to S -ALE is proposed, which consists of a Facial Expression Features Pyramid Network (FEFPN) and a Temporal Transformer Encoder (TTE). FEFPN is capable of extracting pyramidal facial expression features, while TTE can effectively capture coarse -grained and finegrained temporal variations of facial sequences. The proposed model is evaluated on six public datasets across three typical S -ALE tasks (engagement prediction, fatigue detection, and pain assessment), and experimental results show that our method is comparable to or outperforms the state-of-the-art algorithms.
Keywords:
Sequence-level affective level estimation
Facial expression features pyramid network
Temporal transformer encoder
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