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"