opencv学习笔记六十六：FisherFace人脸识别算法

简要：

FisherFace是基于LDA降维的人脸识别算法，由Ronald Fisher最早提出，故以此为名。它和PCA类似，都是将原始数据映射到低维空间，但和PCA最大的区别就是它考虑了降维后数据的类间方差和类内方差，使得降维后的数据类间方差最大，类内方差最小，而PCA是使整体数据降维后的方差最大，没有考虑降维后类间的变化。这又让我想到了二值化中的自适应阈值法，跟LDA的原理有点类似，依次遍历阈值，对于阈值分割后的黑白像素两类，使其两类间灰度值的方差最大，找到的这个阈值即为最适应阈值。

LDA原理：

#include<opencv2\opencv.hpp>
#include<opencv2\face.hpp>
using namespace cv;
using namespace face;
using namespace std;
char win_title[40] = {};

int main(int arc, char** argv) { 
	namedWindow("input",CV_WINDOW_AUTOSIZE);

	//读入模型需要输入的数据，用来训练的图像vector<Mat>images和标签vector<int>labels
	string filename = string("path.txt");
	ifstream file(filename);
	if (!file) { printf("could not load file"); }
	vector<Mat>images;
	vector<int>labels;
	char separator = ';';
	string line,path, classlabel;
	while (getline(file,line)) {
		stringstream lines(line);
		getline(lines, path, separator);
		getline(lines, classlabel);
		//printf("%d\n", atoi(classlabel.c_str()));
		images.push_back(imread(path, 0));
		labels.push_back(atoi(classlabel.c_str()));//atoi(ASCLL to int)将字符串转换为整数型
	}
	int height = images[0].rows;
	int width = images[0].cols;
	printf("height:%d,width:%d\n", height, width);
	//将最后一个样本作为测试样本
	Mat testSample = images[images.size() - 1];
	int testLabel = labels[labels.size() - 1];
	//删除列表末尾的元素
	images.pop_back();
	labels.pop_back();
	
	//加载，训练，预测
	Ptr<BasicFaceRecognizer> model = FisherFaceRecognizer::create();
	model->train(images, labels);     
	int predictedLabel = model->predict(testSample);
	printf("actual label:%d,predict label :%d\n", testLabel, predictedLabel);

	//计算特征值特征向量及平均值
	Mat vals = model->getEigenValues();//89*1
	printf("%d,%d\n", vals.rows, vals.cols);
	Mat vecs = model->getEigenVectors();//10324*89
	printf("%d,%d\n", vecs.rows, vecs.cols);
	Mat mean = model->getMean();//1*10304
	printf("%d,%d\n", mean.rows, mean.cols);

	//显示平均脸
	Mat meanFace = mean.reshape(1, height);//第一个参数为通道数，第二个参数为多少行
	normalize(meanFace, meanFace, 0, 255, NORM_MINMAX, CV_8UC1);
	imshow("Mean Face", meanFace);

	//显示特征脸
	for (int i = 0; i<min(10, vecs.cols); i++) {
		Mat feature_vec = vecs.col(i).clone();
		Mat feature_face= feature_vec.reshape(1, height);	
		normalize(feature_face, feature_face, 0, 255, NORM_MINMAX, CV_8UC1);	
		Mat colorface;
		applyColorMap(feature_face, colorface, COLORMAP_JET);
		
		sprintf_s(win_title, "fisherface%d", i);
		imshow(win_title, colorface);
	}

	//重建人脸
	for (int i = min(0, vecs.cols); i <min(16, vals.cols); i++) {
		Mat vecs_space = vecs.col(i);
		Mat projection = LDA::subspaceProject(vecs_space, mean, images[0].reshape(1, 1));
		Mat reconstruction = LDA::subspaceReconstruct(vecs_space, mean, projection);
		Mat result = reconstruction.reshape(1, height);
		normalize(result, result, 0, 255, NORM_MINMAX, CV_8UC1);
		//char wintitle[40] = {};
		sprintf_s(win_title, "recon face %d", i);
		imshow(win_title, result);
	}

	waitKey(0);
	return 0;
}

平均脸

特征脸

重建脸

参考文献： http://www.cnblogs.com/pinard/p/6244265.html