细粒度表情识别任务因其包含更丰富真实的人类情感而备受关注.现有面部表情识别算法通过提取局部关键区域等方式学习更优的图像表征.然而,这些方法忽略了图像数据集内在的结构关系,且没有充分利用标签间的语义关联度以及图像和标签间的相关性,导致所学特征带来的性能提升有限.其次,现有细粒度表情识别方法并未有效利用和挖掘粗细粒度的层级关系,因而限制了模型的识别性能.此外,现有细粒度表情识别算法忽略了由于标注主观性和情感复杂性导致的标签歧义性问题,极大影响了模型的识别性能.针对上述问题,本文提出一种基于关系感知和标签消歧的细粒度面部表情识别算法(fine-grained facial expression recognition algorithm based on Relationship-Awareness and Label Disambiguation,RALD).该算法通过构建层级感知的图像特征增强网络,充分挖掘图像之间、层级标签之间以及图像和标签之间的依赖关系,以获得更具辨别性的图像特征.针对标签歧义性问题,算法设计了基于近邻样本的标签分布学习模块,通过整合邻域信息进行标签消歧,进一步提升模型识别性能.在细粒度表情识别数据集FG-Emotions上算法的准确度达到97.34%,在粗粒度表情识别数据集RAF-DB上比现有主流表情分类方法提高了0.80%～4.55%.
Fine-Grained Facial Expression Recognition Algorithm Based on Relationship-Awareness and Label Disambiguation
There has been a growing interest in fine-grained facial expression recognition due to its ability to capture more subtle and realistic human emotions.Existing facial expression recognition algorithms enhance image representations by extracting local key regions and other relevant features.However,these methods disregard the inherent structural relationships within the image dataset and fail to fully exploit the semantic correlation between labels and the relationship between images and labels,which restricts the enhancement of feature learning.Besides,current fine-grained expression recognition methods do not ef-fectively explore and utilize the hierarchical relationship between coarse and fine-grained levels,which limits the recognition performance of the model.In addition,existing fine-grained expression recognition algorithms ignore the label ambiguity problem caused by labeling subjectivity and emotional complexity,which greatly affects the recognition performance of the model.To address these issues,we propose a fine-grained facial expression recognition algorithm based on relationship-awareness and label disambiguation(RALD).This algorithm enhances image features by constructing a hierarchy-aware image feature enhancement network,thoroughly exploring the dependencies among images,hierarchical labels,and between images and labels to obtain more discriminative image features.As for the issue of label ambiguity,this algorithm designs a nearest neighbors-based label distribution learning module,which further improves recognition performance by integrating neighborhood information for label disambiguation.Our algorithm achieves 97.34%in terms of accuracy on the FG-Emotions dataset for fine-grained expression recognition.Additionally,it outperforms existing mainstream facial expression recognition algorithms by 0.80%to 4.55%on the RAF-DB dataset for coarse-grained expression recognition.
fine-grained facial expression recognitionattention mechanismrelation awarenessfeature optimiza-tionlabel distribution learning
万方数据
维普
