/// <summary>
/// Face recognition based on Priciple Component Analysis (PCA)
/// classifier using eigenfaces
/// </summary>
/// <param name="labels">The set of labels in the training set</param>
/// <param name="trainingImages">The set of images(faces) in the
/// training set</param>
/// <param name="face">The face detected in gray scale
/// to be recognized. The dimension of the image must be
/// equal to the dimension of the images in the training set</param>
/// <returns>A string representing the label of the face recognized
/// or an empty string if no matches were found</returns>
public String recognizeEigenFace(List <String> labels,
List <Image <Gray, Byte> > trainingImages,
Bitmap face)
{
String label = String.Empty;
int numTraining = trainingImages.ToArray().Length;
InitParams();
Image <Bgr, Byte> imageEmgu = new Image <Bgr, Byte>(face);
Image <Gray, Byte> extractedFace = imageEmgu.Convert <Gray, Byte>().Copy().Resize(
100, 100, INTER.CV_INTER_CUBIC);
extractedFace._EqualizeHist();
if (numTraining != 0)
{
//TermCriteria for face recognition with numbers of trained
//images like maxIteration
MCvTermCriteria termCrit = new MCvTermCriteria(numTraining, Eps);
//Eigen face recognizer
EigenObjectRecognizer recognizer = new EigenObjectRecognizer(
trainingImages.ToArray(),
labels.ToArray(),
Treshold,
ref termCrit);
EigenObjectRecognizer.RecognitionResult r;
r = recognizer.Recognize(extractedFace);
if (r != null)
{
label = r.Label;
MostSimilarFace = trainingImages[r.Index];
MostSimilarFaceIndex = r.Index;
MostSimilarFaceDistance = r.Distance;
MostSimilarFaceLabel = r.Label;
}
else
{
float[] distances = recognizer.GetEigenDistances(extractedFace);
int minIndex = 0;
float minVal = distances[0];
for (int i = 1; i < distances.Length; i++)
{
if (distances[i] < minVal)
{
minVal = distances[i];
minIndex = i;
}
MostSimilarFace = trainingImages[minIndex];
MostSimilarFaceIndex = minIndex;
MostSimilarFaceDistance = minVal;
MostSimilarFaceLabel = labels[minIndex];
}
}
}
return(label);
}
public void capture()
{
this.log("capturing...");
Image <Bgr, Byte> currentFrame = grabber.QueryFrame().Resize(320, 240, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
Image <Gray, byte> gray = currentFrame.Convert <Gray, Byte>();
this.log("capture done");
// check train done
if (this.frameCount % 10 == 0 && this.trainCount >= 0 && this.trainCount < TRAIN_THRESHOLD)
{
if (this.train(gray, this.personToTrain))
{
this.trainCount++;
this.log("training..." + trainCount);
}
this.match("", TRAINING);
return;
}
else if (this.trainCount == TRAIN_THRESHOLD)
{
MCvTermCriteria termCrit = new MCvTermCriteria(50, 0.001);
recognizer = new EigenObjectRecognizer(this.appPath, trainingImages.ToArray(), this.labels.ToArray(), 5000, ref termCrit);
this.trainComplete();
this.trainCount = -1;
}
// Face detect
this.log("face detecting...");
Image <Gray, byte> result;
MCvAvgComp[][] facesDetected = gray.DetectHaarCascade(face, 1.2, 10, Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING, new Size(20, 20));
this.log("face detecting done");
// find the best matched again.
if (facesDetected[0].Length != 1)
{
this.log("no faces detected");
this.match(this.bestMatchPerson, 0);
if (this.frameCount % NOFACE_COUNT == 0)
{
this.matchPersons.Clear();
this.bestMatchPerson = "";
this.log("reset");
this.match("", 0);
// clean up all apps when no use
if (this.frameCount % CLEANUP_COUNT == 0)
{
this.cleanup();
}
}
}
else
{
this.log("faces detected");
foreach (MCvAvgComp f in facesDetected[0])
{
result = currentFrame.Copy(f.rect).Convert <Gray, byte>().Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
// Find the best match person
int testCount = 0;
foreach (KeyValuePair <string, int> iter in this.matchPersons)
{
testCount += iter.Value;
}
if (trainingImages.ToArray().Length != 0)
{
string name = recognizer.Recognize(result, testCount, TRAIN_THRESHOLD);
this.log("found:" + name + ", maxCount:" + testCount);
// Find the best match until threshold
if (testCount < MATCH_THRESHOLD)
{
if (this.matchPersons.ContainsKey(name))
{
this.matchPersons[name]++;
}
else
{
this.matchPersons[name] = 1;
}
this.match("", testCount);
}
else
{
IEnumerable <KeyValuePair <string, int> > sortedDict = from entry in matchPersons orderby entry.Value descending select entry;
string matchPersonsOut = "";
string bestMatch = "";
int i = 0;
foreach (KeyValuePair <string, int> iter in sortedDict)
{
matchPersonsOut += iter.Key + ": " + iter.Value + "\n";
if (i++ == 0)
{
if (iter.Value >= 7)
{
bestMatch = iter.Key;
this.bestMatchPerson = bestMatch;
}
else
{
this.matchPersons.Clear();
this.bestMatchPerson = "";
this.log("cannot find the person over 7");
}
}
}
this.match(bestMatch, testCount);
this.logger(matchPersonsOut);
}
}
}
}
this.frameCount++;
}