Abstract:

In order to further improve the accuracy of facial expression recognition in small samples, a small sample expression recognition method based on deep learning and fusion of different models is proposed. In this method, a single CNN (convolutional neural network) model is compared, and the relatively appropriate CNN is selected by preserving probability [p] of different nodes in the dropout layer. Then, the scale-invariant feature transformation (SIFT) algorithm is used to extract features. The purpose of extracting features with SIFT is to improve the performance of small data. And then, in order to reduce the error, and avoid over fitting, all the models are carried on summary，and the model CNN-SIFT-AVG (convolutional neural network and scale-invariant feature trans-formation average) is obtained by simple average model fusion method. Finally, only a few sample data are used to train the model. The model is tested on FER2013, CK+ and JAFFE datasets. Experimental results show that this model can greatly improve the accuracy of small sample facial expression recognition, and produce excellent results in FER2013, CK+ and JAFFE datasets, with a maximum improvement of about 6% compared with other facial expression recognition methods.

Key words: facial expression recognition (FER), deep learning, scale-invariant feature transformation (SIFT), model fusion, small sample

摘要：

为了进一步提高人脸表情识别在小样本中的准确率，提出了一种深度学习下融合不同模型的小样本表情识别方法。该方法首先对单个卷积神经网络（CNN）模型进行比较，通过dropout层不同的节点保留概率[p，]筛选相对合适的CNN。之后采用尺度不变特征变换（SIFT）算法提取出特征，使用SIFT提取特征的目的是提高小数据的性能。为了减少误差，避免过拟合，将所有模型进行汇总，采用简单平均的模型融合方法得到CNN-SIFT-AVG模型。最后，只采用少量样本数据来训练模型即可。该模型已在FER2013、CK+和JAFFE数据集上进行了验证实验。实验结果表明，该模型可以很大程度上提高小样本表情识别的准确率，并在FER2013、CK+和JAFFE数据集上产生了较优异的结果，与其他表情识别方法相比，准确率最大提升约6%。

关键词: 人脸表情识别（FER）, 深度学习, 尺度不变特征变换（SIFT）, 模型融合, 小样本