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Spatial-Temporal Graphs Plus Transformers for Geometry-Guided Facial Expression Recognition
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AI
DOI：10.1109/TAFFC.2022.3181736.png)
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Abstract
En 中文
Facial expression recognition (FER) is of great interest to the current studies of human-computer interaction. In this paper, we propose a novel geometry-guided facial expression recognition framework, based on graph convolutional networks and transformers, to perform effective emotion recognition from videos. Specifically, we detect and utilize facial landmarks to construct a spatial-temporal graph, based on both the landmark coordinates and local appearance, for representing a facial expression sequence. The graph convolutional blocks and transformer modules are employed to produce high-semantic emotion-related representations from the structured facial graphs, which facilitate the framework to establish both the local and non-local dependency between the vertices. Moreover, spatial and temporal attention mechanisms are introduced into graph-based learning to promote FER reasoning, via the emphasis on the most informative facial components and frames. Extensive experiments demonstrate that the proposed framework achieves promising performance for geometry-based FER and shows great generalization and robustness in real-world applications.
Keywords:
Feature extraction
Facial expression recognition
spatial-temporal graph convolutional network
spatial-temporal transformer
attention mechanism
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