论文标题
使用肢体语言数据集的嘈杂学生培训改善面部表情识别
Noisy Student Training using Body Language Dataset Improves Facial Expression Recognition
论文作者
论文摘要
由于缺乏大量标记的培训数据，野外视频的面部表情识别是一项艰巨的任务。大型DNN（深神经网络）体系结构和集合方法的性能提高了性能，但由于数据不足，很快在某个时候达到了饱和。在本文中，我们使用一种自我训练方法，该方法利用标签数据集和未标记数据集的组合（Body Langue Dataset -BOLD）。实验分析表明，训练嘈杂的学生网络迭代有助于取得更好的结果。此外，我们的模型将面部的不同区域隔离，并使用多层注意机制独立处理它们，从而进一步提高了性能。我们的结果表明，与其他单个型号相比，该提出的方法在基准数据集CK+和AFEW 8.0上实现了最先进的性能。
Facial expression recognition from videos in the wild is a challenging task due to the lack of abundant labelled training data. Large DNN (deep neural network) architectures and ensemble methods have resulted in better performance, but soon reach saturation at some point due to data inadequacy. In this paper, we use a self-training method that utilizes a combination of a labelled dataset and an unlabelled dataset (Body Language Dataset - BoLD). Experimental analysis shows that training a noisy student network iteratively helps in achieving significantly better results. Additionally, our model isolates different regions of the face and processes them independently using a multi-level attention mechanism which further boosts the performance. Our results show that the proposed method achieves state-of-the-art performance on benchmark datasets CK+ and AFEW 8.0 when compared to other single models.
