基于KPCANet和线性判别分析的表情识别

Kernel principal component analysis network and linear discriminate analysis for expression recognition

针对目前多数表情识别算法都是基于浅层特征的,很难达到良好的识别效果,并且核主成分分析网络(PCANet)网络存在提取到的表情特征维数比较高致使识别时间较长和分类效率较低的问题,受到深度学习模型PCANet的启发,提出了一种结合核主成分分析网络(KPCANet)和线性判别分析(LDA)的表情识别算法.首先,利用基于KPCANet模型获取训练样本及测试样本的深层特征;然后,用LDA监督层对KPCANet模型获取的深层特征对表情图像特征进行监督投影,从而使表情特征具有类别区分性;最后,将经LDA投影的特征矩阵输入支持向量机(SVM)中对表情特征进行训练和分类.提出的KPCANet-LDA算法模型在人脸表情数据库CK+和JAFFE上进行实验,实验结果表明提出的算法具有良好的鲁棒性且识别率高于其他对比算法.

The most existing methods of facial expression recognition only extract low-level features,can not obtain satisfactory recognition results.The high dimension of features extracted by the principal component analysis network(PCANet)leads to high memory consumption and low classification efficiency.For solving these problems,inspired by PCANet model,a facial expression recognition method combining KPCANet(kernel principal component analysis network)and LDA(linear discriminate analysis)was proposed.Firstly,KPCANet model was used to get the high-level features of training samples and testing samples.Then,the LDA matrix was used to project the KPCANet features into a low-dimensional space to make the projected features more distinguishable for classification.Finally,the feature matrixs projected by LDA were used to train support vector machine(SVM)and classify the expression images.This proposed algorithm was tested on CK+and JAFFE databases of facial expression.The experimental results show that the proposed algorithm has good robustness and higher recognition rate.