OpenCV中的人脸识别API

今天打算用几种基准算法做人脸识别的测试，突然想到用OpenCV里边的API，查了一下文档，发现竟然还提供了3种方法：EigenFace，FisherFace，LBP。果然是强大呀。

1.建立人脸识别器

createEigenFaceRecognizer

C++: Ptr<FaceRecognizer> createEigenFaceRecognizer(intnum_components=0, double threshold=DBL_MAX) ¶

Parameters:	num_components – The number of components (read: Eigenfaces) kept for this Prinicpal Component Analysis. As a hint: There’s no rule how many components (read: Eigenfaces) should be kept for good reconstruction capabilities. It is based on your input data, so experiment with the number. Keeping 80 components should almost always be sufficient. threshold – The threshold applied in the prediciton.

createFisherFaceRecognizer

C++: Ptr<FaceRecognizer> createFisherFaceRecognizer (int num_components=0, double threshold=DBL_MAX )

Parameters:

num_components – The number of components (read: Fisherfaces) kept for this Linear Discriminant Analysis with the Fisherfaces criterion. It’s useful to keep all components, that means the number of your classesc (read: subjects, persons you want to recognize). If you leave this at the default (0) or set it to a value less-equal0 or greater (c-1), it will be set to the correct number (c-1) automatically. threshold – The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1.

createLBPHFaceRecognizer

C++: Ptr<FaceRecognizer> createLBPHFaceRecognizer (int radius=1, int neighbors=8, int grid_x=8, int grid_y=8, double threshold=DBL_MAX )

Parameters:

radius – The radius used for building the Circular Local Binary Pattern. The greater the radius, the neighbors – The number of sample points to build a Circular Local Binary Pattern from. An appropriate value is to use `` 8`` sample points. Keep in mind: the more sample points you include, the higher the computational cost. grid_x – The number of cells in the horizontal direction, 8 is a common value used in publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector. grid_y – The number of cells in the vertical direction, 8 is a common value used in publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector. threshold – The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1.

 

2.训练

C++: void FaceRecognizer::train(InputArrayOfArrayssrc, InputArray labels) = 0¶

Parameters:	src – The training images, that means the faces you want to learn. The data has to be given as avector<Mat>. labels – The labels corresponding to the images have to be given either as avector<int> or a

 

3.预测

C++: int FaceRecognizer:: predict (InputArray src ) const = 0 ¶

C++: void FaceRecognizer:: predict (InputArray src, int& label, double& confidence ) const = 0 ¶

Predicts a label and associated confidence (e.g. distance) for a given input image.

Parameters:	src – Sample image to get a prediction from. label – The predicted label for the given image. confidence – Associated confidence (e.g. distance) for the predicted label.

简单应用：

int FR::FR_LBPH()
{
	if(trainImages.size()<=0)
	{
		cout<<"please read train data first!"<<endl;
		return 1;
	}
	Ptr<FaceRecognizer> model=createLBPHFaceRecognizer(2,8,10,14);
	model->train(trainImages,trainLabels);
	int plabel= -1;
	double predicted_confidence = 0.0;	
	double correct=0;
	for(int i=0;i<testImages.size();i++)
	{
		model->predict(testImages[i],plabel,predicted_confidence);
		if(plabel==testLabels[i])
		{
			correct++;
		}
		else
		{
			cout<<"name:"<<testNames[i]<<" label:"<<testLabels[i]<<" plabel:"<<plabel<<endl;
		}
	}
	cout<<correct/testImages.size()<<endl;
	return 0;
}

注意：包含头文件#include "opencv2/contrib/contrib.hpp"