一种基于降噪自编码器的人脸表情识别方法

针对传统人脸表情识别算法鲁棒性差，易受到人脸身份信息干扰的问题，本文在基于降噪自编码器的基础上，提出一种人脸表情识别算法。首先，从图片中检测出人脸部分，并进行尺度归一化处理；再构造堆栈式降噪自编码神经网络模型进行预训练；最后为了避免由训练样本不足容易造成的过拟合问题，在深度网络模型的全连接层采用了Dropout技术。实验结果在数据集CK 、JAFFE和Yale上均取得了较高的准确率，说明了该方法具有较强的鲁棒性和抗身份信息干扰的能力。

A method of facial expression recognition based on Denoising AutoEncoders

Considering the problems of the traditional facial expression recognition algorithm with poor robustness and easily disturbed by the face identity information, this paper proposed a face recognition algorithm based on Denoising AutoEncoders. First, the method detected face area from per picture and maked sure all of the face areas are the same scale. Then it constructed the model based on stacked Denoising AutoEncoders for pre-training data. Finally, in order to avoid the overfitting problem that was caused by the lack of training samples, it adopted the Dropout technology in the last full connection layer of the deep network model. The experimental results on CK database, JAFFE database and Yale database achieved the high accuracy rate. So this method has stronger robustness and capacity of resisting disturbance of the face identity information.