In recent years, deep neural networks have been widely concerned by researchers in facial expression recognition. However, insufficient facial training data of the public available database is a major challenge in deep learning, which will lead to an obvious decrease in the effectiveness of learning resu many data augmentation techniques have thus been widely used to enrich the training dataset. In this paper, we introduce a contextual loss function to construct a Contextual Generation Adversarial Network with one generator and one discriminator. The proposed method can map the neutral expression to six basic expressions to expand the database. The experimental results on CK + and KDEF databases show that the proposed method can effectively improve the ability to extract facial features and the ability to generate higher quality images. The data augmentation used the proposed method improves the recognition rate of facial expressions on KDEF and CK + datasets.
展开▼
机译:近年来,深度神经网络被面部表情识别的研究人员广泛关注。但是,公共可用数据库的面部培训数据不足是深度学习的主要挑战,这将导致学习结果的有效性下降;因此,许多数据增强技术已被广泛用于丰富训练数据集。在本文中,我们引入了一种上下文损失功能,以构建具有一个生成器和一个鉴别器的上下文产生的对抗性网络。该方法可以将中性表达式映射到六个基本表达式以扩展数据库。 CK +和KDEF数据库的实验结果表明,该方法可以有效地提高提取面部特征的能力和产生更高质量图像的能力。使用该方法的数据增强提高了KDEF和CK +数据集上的面部表情的识别率。
展开▼