The hindering problem in facial expression recognition (FER) is the presence of inaccurate annotations referred to as noisy annotations in the datasets. These noisy annotations are present in the datasets inherently because the labeling is subjective to the annotator, clarity of the image, etc. Recent works use sample selection methods to solve this noisy annotation problem in FER. In our work, we use a dynamic adaptive threshold to separate confident samples from non-confident ones so that our learning won't be hampered due to non-confident samples. Instead of discarding the non-confident samples, we impose consistency in the negative classes of those non-confident samples to guide the model to learn better in the positive class. Since FER datasets usually come with 7 or 8 classes, we can correctly guess a negative class by 85% probability even by choosing randomly. By learning "which class a sample doesn't belong to", the model can learn "which class it belongs to" in a better manner. We demonstrate proposed framework's effectiveness using quantitative as well as qualitative results. Our method performs better than the baseline by a margin of 4% to 28% on RAFDB and 3.3% to 31.4% on FERPlus for various levels of synthetic noisy labels in the aforementioned datasets.

翻译：面部表情识别（FER）中的主要障碍是数据集中存在不准确标注，即所谓噪声标注。由于标注结果受标注者主观性、图像清晰度等因素影响，这些噪声标注天然存在于数据集中。近年研究采用样本选择方法解决FER中的噪声标注问题。本研究使用动态自适应阈值将置信样本与非置信样本分离，以避免非置信样本妨碍学习过程。我们并未舍弃非置信样本，而是约束这些样本的负类一致性，引导模型更好地学习正类特征。由于FER数据集通常包含7至8个类别，即使随机选择负类，其正确概率也高达85%。通过学习"样本不属于哪个类别"，模型能更优地推断"样本属于哪个类别"。我们通过定量和定性结果验证了所提框架的有效性。在RAFDB和FERPlus数据集的合成噪声标注实验中，本方法相比基线模型性能分别提升4%-28%和3.3%-31.4%。