基于迁移学习的无监督跨域人脸表情识别

本文主要研究了基于迁移学习的无监督跨域人脸表情识别。在过去的几年里，提出的许多方法在人脸表情识别方面取得了令人满意的识别效果。但这些方法通常认为训练和测试数据来自同一个数据集，因此其具有相同的分布。而在实际应用中，这一假设通常并不成立，特别当训练集和测试集来自不同的数据集时，即跨域人脸表情识别问题。为了解决这一问题，本文提出将一种基于联合分布对齐的迁移学习方法(domain align learning)应用于跨域人脸表情识别，该方法通过找到一个特征变换，将源域和目标域数据映射到一个公共子空间中，在该子空间中联合对齐边缘分布和条件分布来减小域之间的分布差异，然后对变换后的特征进行训练得到一个域适应分类器来预测目标域样本标签。为了验证提出算法的有效性，在CK+、Oulu-CASIA NIR和Oulu-CASIA VIS这3个不同的数据库上做了大量实验，实验结果证明所提算法在跨域表情识别上是有效性的。

Unsupervised cross-domain expression recognition based on transfer learning

This paper primarily studies unsupervised cross-domain facial expression recognition based on transfer learning. In recent years, many proposed methods have achieved satisfactory results in facial expression recognition. However, these methods usually assume that the training and test data come from the same data set and therefore have the same distribution. In practical applications, this assumption is usually untrue, especially when the training and test sets come from different data sets, also known as the cross-domain facial expression recognition problem. To solve this problem, we propose a migration learning method (domain align learning) based on joint distributed alignment for cross-domain facial expression recognition. By determining a feature transform, the source and target domain data are mapped onto a common subspace, wherein edge distribution and conditional distribution are aligned jointly to reduce the distribution difference between domains, and then a domain adaptive classifier is obtained by training the transformed features to predict the target domain sample label. To verify the effectiveness of the proposed algorithm, many experiments are performed on three databases, CK+, Oulu-CASIA NIR, and Oulu-CASIA VIS. The experimental results show the effectiveness of the proposed algorithm in cross-domain facial expression recognition.