OpenCV
3.1.0-dev
Open Source Computer Vision
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Classes | |
class | cv::face::BasicFaceRecognizer |
class | cv::face::FaceRecognizer |
Abstract base class for all face recognition models. More... | |
class | cv::face::LBPHFaceRecognizer |
class | cv::face::PredictCollector |
Abstract base class for all strategies of prediction result handling. More... | |
class | cv::face::StandardCollector |
Default predict collector. More... | |
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 |
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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 |
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) |
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 less-equal 0 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. |
Ptr<LBPHFaceRecognizer> cv::face::createLBPHFaceRecognizer | ( | int | radius = 1 , |
int | neighbors = 8 , |
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int | grid_x = 8 , |
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int | grid_y = 8 , |
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double | threshold = DBL_MAX |
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) |
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. |