基于随机权重分配策略的面目表情识别

为了提高面部表情识别的精确度，提出了一种基于数据增强策略面部表情识别，区别于普通的在线随机数据增强，将实验用到的训练数据集采用附加不同的权重分配策略进行增强数据，并随机生成每次训练时的权重，保证其训练数据的多样性并通过比较实验结果得出哪种权重的分布策略适用于面部表情识别数据集的增强，同时解决了面部表情识别因数据集缺乏多样性识别精度不高等问题，提升了人脸表情识别的准确性和鲁棒性，此外还利用VGG19特征提取网络，通过从数据中学习鲁棒性和区分性特征，来实现高精度的面部表情识别。实验结果表明，使用该方式增强后的数据进行训练的模型在Fer2013和扩展Cohn-Kanade （CK+）数据库上对7种表情的识别率相比其原始数据集均有提升。

Facial expression recognition based on random weight assignment strategy

In order to improve the accuracy of facial expression recognition, a facial expression recognition based on data enhancement strategy is proposed. The training data set used in the experiment is enhanced by adding different weight allocation strategies. The weights of each training are randomly generated to ensure the diversity of the training data, and which weight distribution strategy is suitable for the enhancement of facial expression recognition data by comparing the experimental results. At the same time, it solves the problem of facial expression recognition due to the lack of diversity and low accuracy of facial expression recognition, and improves the accuracy and robustness of facial expression recognition. In addition, it also uses VGG19 feature extraction network to achieve high-precision facial expression recognition by learning robustness and distinguishing features from the data. The experimental results show that the model trained by the enhanced data in this way can improve the recognition rate of seven kinds of facial expressions on Fer2013 and extended Cohn-Kanade (CK+) database compared with the original data set.