基于多层感知机改进型Xception人脸表情识别

Improved Xception Facial Expression Recognition Based on MLP

针对使用深度学习提取人脸表情图像特征时易出现冗余特征,提出了一种基于多层感知机(MLP)的改进型Xception人脸表情识别网络.该模型将Xception网络提取的特征输入至多层感知机中进行加权处理,提取出主要特征,滤除冗余特征,从而使得识别准确率得到提升.首先将图像缩放为48*48,然后对数据集进行增强处理,再将这些经过处理的图片送入本文所提网络模型中.消融实验对比表明:本文模型在CK+数据集、JAFFE数据集和MMI数据集上的正确识别率分别为98.991%、99.02%和80.339%,Xception模型在CK+数据集、JAFFE数据集和MMI数据集上的正确识别率分别为97.4829%、90.476%和74.0678%,Xception+2lay模型在CK+数据集、JAFFE数据集和MMI数据集上的正确识别率分别为98.04%、84.06%和75.593%.通过以上消融实验对比,本文方法的识别正确率明显优于Xception模型与Xception+2lay模型.与其他模型相比较也验证了本文模型的有效性.

Aiming at the problem of redundant features when using deep learning to extract facial expression image features,an improved Xception facial expression recognition network based on multi-layer perceptron(MLP)is proposed. In this model,the features extracted from the Xception network are input into the multi-layer perceptron for weighting,the main features are extracted,and the redundant features are filtered out so that the recognition accuracy is improved. First,the image is scaled to 48*48,then the data set is enhanced,and these processed images are fed into the network model proposed in this paper. A comparison of ablation experiments show that:The correct recognition rates of this model on the CK + dataset,JAFFE dataset,and MMI dataset are 98.991%,99.02% and80.339% respectively. The correct recognition rates of Xception model on the CK + dataset,JAFFE dataset and MMI dataset are 97.4829%,90.476%,and 74.0678%,respectively. The correct recognition rates of the Xception + 2lay model on the CK + dataset,JAFFE dataset and MMI dataset are 98.04% and 74.0678%,84.06%,and 75.593%,respectively. By comparing the above ablation experiments,the recognition accuracy of this method is significantly better than the Xception model and the Xception + 2lay model. Compared with other models,the effectiveness of this model is also verified.