Video sequence-based human facial expression recognition using Transformer networks
Bibliographic record
Abstract
目的相比于静态人脸表情图像识别，视频序列中的各帧人脸表情强度差异较大，并且含有中性表情的帧数较多，然而现有模型无法为视频序列中每帧图像分配合适的权重。为了充分利用视频序列中的时空维度信息和不同帧图像对视频表情识别的作用力差异特点，本文提出一种基于Transformer的视频序列表情识别方法。方法首先，将一个视频序列分成含有固定帧数的短视频片段，并采用深度残差网络对视频片段中的每帧图像学习出高层次的人脸表情特征，从而生成一个固定维度的视频片段空间特征。然后，通过设计合适的长短时记忆网络(long short-term memory network，LSTM)和Transformer模型分别从该视频片段空间特征序列中进一步学习出高层次的时间维度特征和注意力特征，并进行级联输入到全连接层，从而输出该视频片段的表情分类分数值。最后，将一个视频所有片段的表情分类分数值进行最大池化，实现该视频的最终表情分类任务。结果在公开的BAUM-1s(Bahcesehir University multimodal)和RML(Ryerson Multimedia Lab)视频情感数据集上的试验结果表明，该方法分别取得了60.72%和75.44%的正确识别率，优于其他对比方法的性能。结论该方法采用端到端的学习方式，能够有效提升视频序列表情识别性能。
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".