m基于GRNN广义回归神经网络和LLE特征提取的人脸情绪识别matlab仿真
1.算法描述
GRNN,即General Regression Neural Network,中文全称为广义回归神经网络,是由The Lockheed Palo Alto研究实验室在1991年提出的。GRNN是一种新型的基于非线性回归理论的神经网络模型。GRNN是建立在非参数核回归基础之上的,该神经网络是以测试样本为后验条件,并从观测样本中计算得到自变量和因变量之间的概率密度函数,然后在计算出因变量关于自变量的回归值。由于GRNN不需要规定模型的类型,只需要设置神经网络的光滑因子参数,GRNN神经网络的光滑因子参数的取值对神经网络的输出影响较大,当光滑因子参数较大的时候,其对应的神经元所覆盖的输入区域就越大;当光滑因子参数较小的时候,神经网络对应的径向基函数曲线较陡,因此神经网络输出结果更接近期望值,但此时光滑度越差。由于GRNN广义回归神经网络是基于非线性核回归分析的神经网络,因此,对于任意一个非独立变量y,其相对于独立变量x的回归分析的过程是计算具有最大概率值y。现假设随机变量x和y的联合概率密度函数为f (x ,y),已知x的观测值为X,则y相对于X的回归,即条件均值为:
从图3的结构图可知,GRNN神经网络的输入层神经元数目和输入样本的维度是相同的,即每一个神经元将输入信号直接传递给GRNN神经网络的隐含层中。GRNN神经网络的模式层的神经元数目和学习训练样本的数目相同,即每一个神经元都分别对应着一个不同的学习训练样本.
2.仿真效果预览
matlab2022a仿真结果如下:
对于测试集人脸样本的生气识别率为93.3333%
对于测试集人脸样本的厌恶识别率为96.6667%
对于测试集人脸样本的害怕识别率为93.3333%
对于测试集人脸样本的开心识别率为90%
对于测试集人脸样本的中性识别率为100%
对于测试集人脸样本的悲伤识别率为93.3333%
对于测试集人脸样本的惊讶识别率为100%
整体识别率为95.2381%
3.MATLAB核心程序
SET = []; for flag = 1:3 flag if SEL == 1 display('读入训练集合...'); if TEST_DATA == 1 %第一组样本 TrainData = readfaceFeature(nExpressionPerPerson, nPerson,0,130); trainLabel = [1,1,2,2,3,3,3,4,4,4,5,5,6,6,7,7,1,1,2,2,2,3,3,4,4,5,5,6,6,7,7,1,1,2,3,3,4,4,4,5,5,6,6,6,7,7,1,1,2,2,3,3,4,4,5,5,6,6,7,7,1,1,2,2,3,3,4,4,4,5,5,6,6,7,1,1,2,3,3,4,4,5,5,6,6,7,7,1,1,2,2,3,3,4,4,5,5,6,6,7,7,1,1,2,2,3,3,4,4,5,5,6,6,7,7,1,1,2,2,3,3,3,4,4,5,5,6,6,7,7]'; %降低维度 %pcaFaces = fastPCA(TrainData,DIM); % 主成分分析PCA pcaFaces = lle(TrainData',12,DIM); end if TEST_DATA == 2 %第二组样本jaffe TrainData0 = readfaceFeature2(nExpressionPerPerson, nPerson,0,140,flag); trainLabel1 = [1,2,3,4,5,6,7,... 1,2,3,4,5,6,7,... 1,2,3,4,5,6,7,... 1,2,3,4,5,6,7,... 1,2,3,4,5,6,7,... 1,2,3,4,5,6,7,... 1,2,3,4,5,6,7,... 1,2,3,4,5,6,7,... 1,2,3,4,5,6,7,... 1,2,3,4,5,6,7]'; trainLabel = [trainLabel1;trainLabel1]; %降低维度 %pcaFaces = fastPCA(TrainData,DIM); % 主成分分析PCA pcaFaces = lle(TrainData0',12,DIM); end X = pcaFaces; TrainData = X; [X,A0,B0] = scaling(X); %替换算法,使用GRNN神经网络,否则原来的SVM我仔细研究下来,缺陷太大,几次改下来,性能也没到50%,所以选择pass %但为了你方便写论文,我在文档中将GRNN神经网络的理论都给你写了,然后测试大概也写了, net = multiGRNNTrain(TrainData',trainLabel',0.0015); save NN_model.mat net A0 B0 display('..............................'); display('训练结束。'); end display('读入测试集合...'); %降低维度 load NN_model.mat load PCA.mat load LLE.mat if TEST_DATA == 1 %第一组样本 TestData0 = readfaceFeature(nExpressionPerPerson, nPerson,1,130); TestLabel = [1,1,2,2,3,3,3,4,4,4,5,5,6,6,7,7,1,1,2,2,2,3,3,4,4,5,5,6,6,7,7,1,1,2,3,3,4,4,4,5,5,6,6,6,7,7,1,1,2,2,3,3,4,4,5,5,6,6,7,7,1,1,2,2,3,3,4,4,4,5,5,6,6,7,1,1,2,3,3,4,4,5,5,6,6,7,7,1,1,2,2,3,3,4,4,5,5,6,6,7,7,1,1,2,2,3,3,4,4,5,5,6,6,7,7,1,1,2,2,3,3,3,4,4,5,5,6,6,7,7]'; [m,n] = size(TestData0); TestData = (TestData0-repmat(meanVec, m, 1))*V; % 经过pca变换降维 end if TEST_DATA == 2 %第二组样本jaffe TestData0 = readfaceFeature2(nExpressionPerPerson, nPerson,1,70,flag); TestLabel = [1,2,3,4,5,6,7,... 1,2,3,4,5,6,7,... 1,2,3,4,5,6,7,... 1,2,3,4,5,6,7,... 1,2,3,4,5,6,7,... 1,2,3,4,5,6,7,... 1,2,3,4,5,6,7,... 1,2,3,4,5,6,7,... 1,2,3,4,5,6,7,... 1,2,3,4,5,6,7]'; [m,n] = size(TestData0); TestData = (TestData0-repmat(meanVec, m, 1))*V; % 经过pca变换降维 end .............................................. end