Class LBPHFaceRecognizer

Namespace

OpenCvSharp.Face

Assembly

OpenCvSharp.dll

Abstract base class for all face recognition models. All face recognition models in OpenCV are derived from the abstract base class FaceRecognizer, which provides a unified access to all face recongition algorithms in OpenCV.

public class LBPHFaceRecognizer : FaceRecognizer, IDisposable

Inheritance

object

CvObject

CvPtrObject

Algorithm

FaceRecognizer

LBPHFaceRecognizer

Implements

IDisposable

Inherited Members

FaceRecognizer.Train(IEnumerable<Mat>, IEnumerable<int>)

FaceRecognizer.Update(IEnumerable<Mat>, IEnumerable<int>)

FaceRecognizer.Predict(InputArray)

FaceRecognizer.Predict(InputArray, out int, out double)

FaceRecognizer.Predict(InputArray, PredictCollector)

FaceRecognizer.Write(string)

FaceRecognizer.Read(string)

FaceRecognizer.Write(FileStorage)

FaceRecognizer.Read(FileNode)

FaceRecognizer.SetLabelInfo(int, string)

FaceRecognizer.GetLabelInfo(int)

FaceRecognizer.GetLabelsByString(string)

Algorithm.Empty

Algorithm.Save(string)

Algorithm.GetDefaultName()

CvPtrObject.RawPtr

CvObject.IsDisposed

CvObject.ptr

CvObject.SetSafeHandle(OpenCvSafeHandle)

CvObject.SetPostReleaseAction(Action)

CvObject.Dispose()

CvObject.Dispose(bool)

CvObject.DisposeManaged()

CvObject.DisposeUnmanaged()

CvObject.ThrowIfDisposed()

CvObject.CvPtr

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Methods

Create(int, int, int, int, double)

The Circular Local Binary Patterns (used in training and prediction) expect the data given as grayscale images, use cvtColor to convert between the color spaces. This model supports updating.

GetGridX()

Gets the number of cells in the horizontal direction used to build the Circular Local Binary Pattern.

GetGridY()

Gets the number of cells in the vertical direction used to build the Circular Local Binary Pattern.

GetHistograms()

Gets the Local Binary Patterns histograms calculated from the given training data (empty if none was given).

GetLabels()

Gets the labels corresponding to the calculated Local Binary Patterns histograms.

GetNeighbors()

Gets the number of sample points used to build the Circular Local Binary Pattern.

GetRadius()

Gets the radius used for building the Circular Local Binary Pattern.

GetThreshold()

Gets the threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, the prediction returns -1.

SetGridX(int)

Sets the number of cells in the horizontal direction used to build the Circular Local Binary Pattern.

SetGridY(int)

Sets the number of cells in the vertical direction used to build the Circular Local Binary Pattern.

SetNeighbors(int)

Sets the number of sample points used to build the Circular Local Binary Pattern. An appropriate value is 8; keep in mind that including more sample points increases the computational cost.

SetRadius(int)

Sets the radius used for building the Circular Local Binary Pattern. The greater the radius, the smoother the image but more spatial information you can get.

SetThreshold(double)

Sets the threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, the prediction returns -1.