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Enhanced spatial-temporal learning network for dynamic facial expression recognition
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AI
DOI：10.1016/j.bspc.2023.105316.png)
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摘要
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The recognition of dynamic facial expressions has received increasing attention since they can better reflect the real expression process of emotion than a static image. However, due to various factors such as subtle variation differences, pose, occlusion, and illumination, it has been a challenging vision task to obtain discriminative expression features in dynamic facial expression recognition. Traditional CNN-based deep learning networks lack global and temporal contextual expression understanding, which tends to affect the final recognition of dynamic expressions. Therefore, we propose an enhanced spatial-temporal learning network (ESTLNet) for more robust dynamic facial expression recognition, which consists of a spatial fusion learning module (SFLM) and a temporal transformer enhancement module (TTEM). First, the SFLM obtains a more expressive spatial feature representation through a dual-channel feature fusion learning module. Then, the TTEM extracts more valid temporal contextual expression features based on the above spatial features through an encoder constructed by cascading a self-attention learning network and an effective gated feed-forward network. Finally, the co-enhanced spatial-temporal model approach is assessed on the four broadly used dynamic expression datasets (DFEW, AFEW, CK+, and Oulu-CASIA). Extensive experimental outcomes demonstrate that our approach surpasses several existing state-of-the-art methods, leading to notable enhancements in performance.
Keyword:
Dynamic facial expression recognition
Enhanced spatial -temporal learning
Feature enhancement
Transformer
期刊
IF:
4.9
论文数:
1.0W
被引数:
2.4W
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