Facial emotion recognition finds a major role in affective computing. Recognizing emotion by facial expression is an extremely important activity to design control oriented and human computer interactive applications especially in cognitive science and neuroscience. For a precise and robust recognition, feature extraction is one of the major challenges in facial expression recognition system. Wavelet transform is one of the major key methods utilized for feature extraction in facial emotion recognition. In this paper, the statistical parameters from the proposed subband selective multilevel stationary wavelet gradient transform are calculated and are utilized as features for efficacious recognition of emotion. The features of the wavelet transform contain both spatial and spectral domain information which is best suited for identifying human emotions through facial expression. The introduction of gradient transform to find the gradient of subband avails to estimate the edges in images for the quality amelioration of subbands. The dimension reduction in the extracted features is done by using Pearson–kernel–principal component analysis method. The classification of emotion using the selected features is done by the proposed Gaussian membership function fuzzy SVM classifier. Experiments were performed on the well-known database for facial expression such as JAFEE database, CK + database and FG Net database and obtained promising emotion classification results.

面部情感识别在情感计算中起着重要作用。通过面部表情识别情绪是设计面向控制的和人机交互应用程序的一项极其重要的活动，尤其是在认知科学和神经科学中。对于精确而鲁棒的识别，特征提取是面部表情识别系统的主要挑战之一。小波变换是面部表情识别中特征提取的主要关键方法之一。在本文中，从所提出的子带选择性多级平稳小波梯度变换中计算统计参数，并将其用作有效识别情绪的特征。小波变换的特征包含空间和光谱域信息，最适合通过面部表情识别人的情绪。引入梯度变换以查找子带的梯度有助于估计图像中的边缘，从而改善子带的质量。通过使用Pearson-kernel-principal分量分析方法来完成提取特征的降维。使用所选择的特征对情绪进行分类是通过提出的高斯隶属函数模糊SVM分类器完成的。在JAFEE数据库，CK +数据库和FG Net数据库等知名的面部表情数据库上进行了实验，获得了有希望的情感分类结果。引入梯度变换以查找子带的梯度有助于估计图像中的边缘，从而改善子带的质量。通过使用Pearson-kernel-principal分量分析方法来完成提取特征的降维。使用所选择的特征对情绪进行分类是通过提出的高斯隶属函数模糊SVM分类器完成的。在JAFEE数据库，CK +数据库和FG Net数据库等知名的面部表情数据库上进行了实验，获得了有希望的情感分类结果。引入梯度变换以查找子带的梯度有助于估计图像中的边缘，从而改善子带的质量。通过使用Pearson-kernel-principal分量分析方法来完成提取特征的降维。使用所选择的特征对情绪进行分类是通过提出的高斯隶属函数模糊SVM分类器完成的。在JAFEE数据库，CK +数据库和FG Net数据库等知名的面部表情数据库上进行了实验，获得了有希望的情感分类结果。使用所选择的特征对情绪进行分类是通过提出的高斯隶属函数模糊SVM分类器完成的。在JAFEE数据库，CK +数据库和FG Net数据库等知名的面部表情数据库上进行了实验，获得了有希望的情感分类结果。使用所选择的特征对情绪进行分类是通过提出的高斯隶属函数模糊SVM分类器完成的。在JAFEE数据库，CK +数据库和FG Net数据库等知名的面部表情数据库上进行了实验，获得了有希望的情感分类结果。