前面我们学习了基于特征脸的人脸识别，现在我们学习一下基于Fisher脸的人脸识别，Fisher人脸识别基于LDA(线性判别算法)算法，算法的详细介绍可以参考下面两篇教程内容：
http://docs.opencv.org/modules/contrib/doc/facerec/facerec_tutorial.html
LDA算法细节参考：
http://www.cnblogs.com/mikewolf2002/p/3435750.html
程序代码:
#include "opencv2/core/core.hpp"
#include "opencv2/contrib/contrib.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <iostream>
#include <fstream>
#include <sstream>
using namespace cv;
using namespace std;
static Mat norm_0_255(InputArray _src)
{
Mat src = _src.getMat();
Mat dst;
switch(src.channels())
{
case 1:
cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
break;
case 3:
cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC3);
break;
default:
src.copyTo(dst);
break;
}
return dst;
}
static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';')
{
std::ifstream file(filename.c_str(), ifstream::in);
if (!file)
{
string error_message = "No valid input file was given, please check the given filename.";
CV_Error(CV_StsBadArg, error_message);
}
string line, path, classlabel;
while (getline(file, line))
{
stringstream liness(line);
getline(liness, path, separator);
getline(liness, classlabel);
if(!path.empty() && !classlabel.empty())
{
images.push_back(imread(path, 0));
labels.push_back(atoi(classlabel.c_str()));
}
}
}
int main(int argc, const char *argv[])
{
string fn_csv = string("facerec_at_t.txt");
vector<Mat> images;
vector<int> labels;
try
{
read_csv(fn_csv, images, labels);
} catch (cv::Exception& e)
{
cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
exit(1);
}
if(images.size() <= 1)
{
string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
CV_Error(CV_StsError, error_message);
}
int height = images[0].rows;
Mat testSample = images[images.size() - 1];
int testLabel = labels[labels.size() - 1];
images.pop_back();
labels.pop_back();
Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
model->train(images, labels);
int predictedLabel = model->predict(testSample);
string result_message = format("Predicted class = %d / Actual class = %d.", predictedLabel, testLabel);
cout << result_message << endl;
Mat eigenvalues = model->getMat("eigenvalues");
Mat W = model->getMat("eigenvectors");
Mat mean = model->getMat("mean");
imshow("mean", norm_0_255(mean.reshape(1, images[0].rows)));
for (int i = 0; i < min(16, W.cols); i++)
{
string msg = format("Eigenvalue #%d = %.5f", i, eigenvalues.at<double>(i));
cout << msg << endl;
Mat ev = W.col(i).clone();
Mat grayscale = norm_0_255(ev.reshape(1, height));
Mat cgrayscale;
applyColorMap(grayscale, cgrayscale, COLORMAP_BONE);
imshow(format("fisherface_%d", i), cgrayscale);
}
for(int num_component = 0; num_component < min(16, W.cols); num_component++)
{
Mat ev = W.col(num_component);
Mat projection = subspaceProject(ev, mean, images[0].reshape(1,1));
Mat reconstruction = subspaceReconstruct(ev, mean, projection);
reconstruction = norm_0_255(reconstruction.reshape(1, images[0].rows));
imshow(format("fisherface_reconstruction_%d", num_component), reconstruction);
}
while(1)
waitKey(0);
return 0;
}
从代码中我们可以看到，最大的区别就是创建人脸识别模式类时候，调用的函数不一样，其它代码和特征脸识别的代码一样，对于train和predict函数来说，调用方式完全一样，只是底层的具体算法细节不一样。
Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
下面是Fisher人脸识别类的train函数，从中可以看到，函数会先调用PCA算法进行降维，之后再执行LDA算法，求得Fisher特征值和特征向量。注意投影矩阵是PCA算法的特征向量和LDA算法特征向量的乘积。
// 先用PCA算法降维perform a PCA and keep (N-C) components
PCA pca(data, Mat(), CV_PCA_DATA_AS_ROW, (N-C));
// 把数据投影到 PCA空间，再对该数据执行LDA算法
LDA lda(pca.project(data),labels, _num_components);
// 保存总的均值向量
_mean = pca.mean.reshape(1,1);
_labels = labels.clone();
lda.eigenvalues().convertTo(_eigenvalues, CV_64FC1);
//计算投影矩阵=pca.eigenvectors * lda.eigenvectors.
// Note: OpenCV stores the eigenvectors by row, so we need to transpose it!
gemm(pca.eigenvectors, lda.eigenvectors(), 1.0, Mat(), 0.0, _eigenvectors, GEMM_1_T);
//把原始矩阵投影到新的投影空间
for(int sampleIdx = 0; sampleIdx < data.rows; sampleIdx++) {
Mat p = subspaceProject(_eigenvectors, _mean, data.row(sampleIdx));
_projections.push_back(p);
}
在程序中，我们仍然使用AT&T Facedatabase数据库的图片，原教程中推荐用Yale Facedatabase A，但是它的图像格式是gif，OpenCV不支持，只好放弃。
程序代码：FirstOpenCV34
