OpenCV
3.0.0
Open Source Computer Vision

Classes  
class  cv::face::BasicFaceRecognizer 
class  cv::face::FaceRecognizer 
Abstract base class for all face recognition models. More...  
class  cv::face::LBPHFaceRecognizer 
Functions  
Ptr< BasicFaceRecognizer >  cv::face::createEigenFaceRecognizer (int num_components=0, double threshold=DBL_MAX) 
Ptr< BasicFaceRecognizer >  cv::face::createFisherFaceRecognizer (int num_components=0, double threshold=DBL_MAX) 
Ptr< LBPHFaceRecognizer >  cv::face::createLBPHFaceRecognizer (int radius=1, int neighbors=8, int grid_x=8, int grid_y=8, double threshold=DBL_MAX) 
Ptr<BasicFaceRecognizer> cv::face::createEigenFaceRecognizer  (  int  num_components = 0 , 
double  threshold = DBL_MAX 

) 
num_components  The number of components (read: Eigenfaces) kept for this Principal 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 prediction. 
Ptr<BasicFaceRecognizer> cv::face::createFisherFaceRecognizer  (  int  num_components = 0 , 
double  threshold = DBL_MAX 

) 
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 classes c (read: subjects, persons you want to recognize). If you leave this at the default (0) or set it to a value lessequal 0 or greater (c1), it will be set to the correct number (c1) automatically. 
threshold  The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns 1. 
Ptr<LBPHFaceRecognizer> cv::face::createLBPHFaceRecognizer  (  int  radius = 1 , 
int  neighbors = 8 , 

int  grid_x = 8 , 

int  grid_y = 8 , 

double  threshold = DBL_MAX 

) 
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. 