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(intnum_components=0, doublethreshold=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, intgrid_y=8, doublethreshold=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, InputArraylabels) = 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(InputArraysrc) const = 0¶

C++: void FaceRecognizer::predict(InputArraysrc, 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"