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unstable face recognition using OpenCV

I've already asked my question in stackoverflow.

I’m developing an android application for face recognition, using JavaCV which is unofficial wrapper of OpenCV. After importing (com.googlecode.javacv.cpp.opencv_contrib.FaceRecognizer) I apply and test the following known methods:

LBPH using createLBPHFaceRecognizer() method
FisherFace using createFisherFaceRecognizer() method
EigenFace using createEigenFaceRecognizer() method

Before I recognize the detected face, I correct the rotated face and crop the proper zone, inspiring from this method

In general when I pass on camera a face already exist in the database, the recognition is ok. But this is not always correct. Sometimes it recognizes the unknown face (not found in Database of trained samples) with a high probability. When we have in the DB two or more faces of similar features (beard, mustache, glasses...) the recognition may be highly mistaken between those faces!

To predict the result using the test face image, I apply the following code:

public String predict(Mat m) {

    int n[] = new int[1];
    double p[] = new double[1];
    IplImage ipl = MatToIplImage(m,WIDTH, HEIGHT);

    faceRecognizer.predict(ipl, n, p);

    if (n[0]!=-1)
     mProb=(int)p[0];
    else
        mProb=-1;
        if (n[0] != -1)
        return labelsFile.get(n[0]);
    else
        return "Unkown";
}

I can’t control the threshold of the probability p, because:

Small p < 50 could predict a correct result.
High p > 70 could predict a false result.
Middle p could predict a correct or false.

As well, I don’t understand why predict() function gives sometime a probability greater than 100 in case of using LBPH??? and in case of Fisher and Eigen it gives very big values (>2000) ?? Can someone help in finding a solution for these bizarre problems? Is there any suggestion to improve robustness of recognition? especially in case of similarity of two different faces.

The following is the entire class using Facerecognizer:

import com.googlecode.javacv.cpp.opencv_imgproc; import com.googlecode.javacv.cpp.opencv_contrib.FaceRecognizer; import com.googlecode.javacv.cpp.opencv_core.IplImage; import com.googlecode.javacv.cpp.opencv_core.MatVector;

import android.graphics.Bitmap; import android.os.Environment; import android.util.Log; import android.widget.Toast;

public class PersonRecognizer {

public final static int MAXIMG = 100;
FaceRecognizer faceRecognizer;
String mPath;
int count=0;
labels labelsFile;

 static  final int WIDTH= 128;
 static  final int HEIGHT= 128;;
 private int mProb=999;


PersonRecognizer(String path)
{
  faceRecognizer =  com.googlecode.javacv.cpp.opencv_contrib.createLBPHFaceRecognizer(2,8,8,8,200);
 // path=Environment.getExternalStorageDirectory()+"/facerecog/faces/";
 mPath=path;
 labelsFile= new labels(mPath);


}

void changeRecognizer(int nRec)
{
    switch(nRec) {
    case 0: faceRecognizer = com.googlecode.javacv.cpp.opencv_contrib.createLBPHFaceRecognizer(1,8,8,8,100);
            break;
    case 1: faceRecognizer = com.googlecode.javacv.cpp.opencv_contrib.createFisherFaceRecognizer();
            break;
    case 2: faceRecognizer = com.googlecode.javacv.cpp.opencv_contrib.createEigenFaceRecognizer();
            break;
    }
    train();

}

void add(Mat m, String description) {
    Bitmap bmp= Bitmap.createBitmap(m.width(), m.height(), Bitmap.Config.ARGB_8888);

    Utils.matToBitmap(m,bmp);
    bmp= Bitmap.createScaledBitmap(bmp, WIDTH, HEIGHT, false);

    FileOutputStream f;
    try {
        f = new FileOutputStream(mPath+description+"-"+count+".jpg",true);
        count++;
        bmp.compress(Bitmap.CompressFormat.JPEG, 100, f);
        f.close();

    } catch (Exception e) {
        Log.e("error",e.getCause()+" "+e.getMessage());
        e.printStackTrace();

    }
}

public boolean train() {

    File root = new File(mPath);
    Log.i("mPath",mPath);
    FilenameFilter pngFilter = new FilenameFilter() {
        public boolean accept(File dir, String name) {
            return name.toLowerCase().endsWith(".jpg");

    };
    };

    File[] imageFiles = root.listFiles(pngFilter);

    MatVector images = new MatVector(imageFiles.length);

    int[] labels = new int[imageFiles.length];

    int counter = 0;
    int label;

    IplImage img=null;
    IplImage grayImg;

    int i1=mPath.length();


    for (File image : imageFiles) {
        String p = image.getAbsolutePath();
        img = cvLoadImage(p);

        if (img==null)
            Log.e("Error","Error cVLoadImage");
        Log.i("image",p);

        int i2=p.lastIndexOf("-");
        int i3=p.lastIndexOf(".");
        int icount=Integer.parseInt(p.substring(i2+1,i3)); 
        if (count<icount) count++;

        String description=p.substring(i1,i2);

        if (labelsFile.get(description)<0)
            labelsFile.add(description, labelsFile.max()+1);

        label = labelsFile.get(description);

        grayImg = IplImage.create(img.width(), img.height(), IPL_DEPTH_8U, 1);

        cvCvtColor(img, grayImg, CV_BGR2GRAY);

        images.put(counter, grayImg);

        labels[counter] = label;

        counter++;
    }
    if (counter>0)
        if (labelsFile.max()>1)
            faceRecognizer.train(images, labels);
    labelsFile.Save();
return true;
}

public boolean canPredict()
{
    if (labelsFile.max()>1)
        return true;
    else
        return false;

}
public String predict(Mat m) {
    if (!canPredict())
        return "";
    int n[] = new int[1];
    double p[] = new double[1];
    IplImage ipl = MatToIplImage(m,WIDTH, HEIGHT);

// IplImage ipl = MatToIplImage(m,-1, -1);

    faceRecognizer.predict(ipl, n, p);

    if (n[0]!=-1)
     mProb=(int)p[0];
    else
        mProb=-1;
//  if ((n[0] != -1)&&(p[0]<95))
    if (n[0] != -1)
        return labelsFile.get(n[0]);
    else
        return "Unkown";
}

  IplImage MatToIplImage(Mat m,int width,int heigth)
  {
       Bitmap bmp=Bitmap.createBitmap(m.width(), m.height(), Bitmap.Config.ARGB_8888);


       Utils.matToBitmap(m, bmp);
       return BitmapToIplImage(bmp,width, heigth);

  }
IplImage BitmapToIplImage(Bitmap bmp, int width, int height) {

    if ((width != -1) || (height != -1)) {
        Bitmap bmp2 = Bitmap.createScaledBitmap(bmp, width, height, false);
        bmp = bmp2;
    }
    IplImage image = IplImage.create(bmp.getWidth(), bmp.getHeight(),
            IPL_DEPTH_8U, 4);

    bmp.copyPixelsToBuffer(image.getByteBuffer());

    IplImage grayImg = IplImage.create(image.width(), image.height(),
            IPL_DEPTH_8U, 1);

    cvCvtColor(image, grayImg, opencv_imgproc.CV_BGR2GRAY);

    return grayImg;
}

protected void SaveBmp(Bitmap bmp,String path)
  {
        FileOutputStream file;
        try {
            file = new FileOutputStream(path , true);
        bmp.compress(Bitmap.CompressFormat.JPEG,100,file);  
        file.close();
        }
        catch (Exception e) {
            // TODO Auto-generated catch block
            Log.e("",e.getMessage()+e.getCause());
            e.printStackTrace();
        }
  }

public void load() {
    train();
}
public int getProb() {
    // TODO Auto-generated method stub
    return mProb;
}

}

click to hide/show revision 2
retagged

updated 2014-04-27 23:59:16 -0500

berak gravatar image

unstable face recognition using OpenCV

I've already asked my question in stackoverflow.

I’m developing an android application for face recognition, using JavaCV which is unofficial wrapper of OpenCV. After importing (com.googlecode.javacv.cpp.opencv_contrib.FaceRecognizer) I apply and test the following known methods:

LBPH using createLBPHFaceRecognizer() method
FisherFace using createFisherFaceRecognizer() method
EigenFace using createEigenFaceRecognizer() method

Before I recognize the detected face, I correct the rotated face and crop the proper zone, inspiring from this method

In general when I pass on camera a face already exist in the database, the recognition is ok. But this is not always correct. Sometimes it recognizes the unknown face (not found in Database of trained samples) with a high probability. When we have in the DB two or more faces of similar features (beard, mustache, glasses...) the recognition may be highly mistaken between those faces!

To predict the result using the test face image, I apply the following code:

public String predict(Mat m) {

    int n[] = new int[1];
    double p[] = new double[1];
    IplImage ipl = MatToIplImage(m,WIDTH, HEIGHT);

    faceRecognizer.predict(ipl, n, p);

    if (n[0]!=-1)
     mProb=(int)p[0];
    else
        mProb=-1;
        if (n[0] != -1)
        return labelsFile.get(n[0]);
    else
        return "Unkown";
}

I can’t control the threshold of the probability p, because:

Small p < 50 could predict a correct result.
High p > 70 could predict a false result.
Middle p could predict a correct or false.

As well, I don’t understand why predict() function gives sometime a probability greater than 100 in case of using LBPH??? and in case of Fisher and Eigen it gives very big values (>2000) ?? Can someone help in finding a solution for these bizarre problems? Is there any suggestion to improve robustness of recognition? especially in case of similarity of two different faces.

The following is the entire class using Facerecognizer:

import com.googlecode.javacv.cpp.opencv_imgproc; import com.googlecode.javacv.cpp.opencv_contrib.FaceRecognizer; import com.googlecode.javacv.cpp.opencv_core.IplImage; import com.googlecode.javacv.cpp.opencv_core.MatVector;

import android.graphics.Bitmap; import android.os.Environment; import android.util.Log; import android.widget.Toast;

public class PersonRecognizer {

public final static int MAXIMG = 100;
FaceRecognizer faceRecognizer;
String mPath;
int count=0;
labels labelsFile;

 static  final int WIDTH= 128;
 static  final int HEIGHT= 128;;
 private int mProb=999;


PersonRecognizer(String path)
{
  faceRecognizer =  com.googlecode.javacv.cpp.opencv_contrib.createLBPHFaceRecognizer(2,8,8,8,200);
 // path=Environment.getExternalStorageDirectory()+"/facerecog/faces/";
 mPath=path;
 labelsFile= new labels(mPath);


}

void changeRecognizer(int nRec)
{
    switch(nRec) {
    case 0: faceRecognizer = com.googlecode.javacv.cpp.opencv_contrib.createLBPHFaceRecognizer(1,8,8,8,100);
            break;
    case 1: faceRecognizer = com.googlecode.javacv.cpp.opencv_contrib.createFisherFaceRecognizer();
            break;
    case 2: faceRecognizer = com.googlecode.javacv.cpp.opencv_contrib.createEigenFaceRecognizer();
            break;
    }
    train();

}

void add(Mat m, String description) {
    Bitmap bmp= Bitmap.createBitmap(m.width(), m.height(), Bitmap.Config.ARGB_8888);

    Utils.matToBitmap(m,bmp);
    bmp= Bitmap.createScaledBitmap(bmp, WIDTH, HEIGHT, false);

    FileOutputStream f;
    try {
        f = new FileOutputStream(mPath+description+"-"+count+".jpg",true);
        count++;
        bmp.compress(Bitmap.CompressFormat.JPEG, 100, f);
        f.close();

    } catch (Exception e) {
        Log.e("error",e.getCause()+" "+e.getMessage());
        e.printStackTrace();

    }
}

public boolean train() {

    File root = new File(mPath);
    Log.i("mPath",mPath);
    FilenameFilter pngFilter = new FilenameFilter() {
        public boolean accept(File dir, String name) {
            return name.toLowerCase().endsWith(".jpg");

    };
    };

    File[] imageFiles = root.listFiles(pngFilter);

    MatVector images = new MatVector(imageFiles.length);

    int[] labels = new int[imageFiles.length];

    int counter = 0;
    int label;

    IplImage img=null;
    IplImage grayImg;

    int i1=mPath.length();


    for (File image : imageFiles) {
        String p = image.getAbsolutePath();
        img = cvLoadImage(p);

        if (img==null)
            Log.e("Error","Error cVLoadImage");
        Log.i("image",p);

        int i2=p.lastIndexOf("-");
        int i3=p.lastIndexOf(".");
        int icount=Integer.parseInt(p.substring(i2+1,i3)); 
        if (count<icount) count++;

        String description=p.substring(i1,i2);

        if (labelsFile.get(description)<0)
            labelsFile.add(description, labelsFile.max()+1);

        label = labelsFile.get(description);

        grayImg = IplImage.create(img.width(), img.height(), IPL_DEPTH_8U, 1);

        cvCvtColor(img, grayImg, CV_BGR2GRAY);

        images.put(counter, grayImg);

        labels[counter] = label;

        counter++;
    }
    if (counter>0)
        if (labelsFile.max()>1)
            faceRecognizer.train(images, labels);
    labelsFile.Save();
return true;
}

public boolean canPredict()
{
    if (labelsFile.max()>1)
        return true;
    else
        return false;

}
public String predict(Mat m) {
    if (!canPredict())
        return "";
    int n[] = new int[1];
    double p[] = new double[1];
    IplImage ipl = MatToIplImage(m,WIDTH, HEIGHT);

// IplImage ipl = MatToIplImage(m,-1, -1);

    faceRecognizer.predict(ipl, n, p);

    if (n[0]!=-1)
     mProb=(int)p[0];
    else
        mProb=-1;
//  if ((n[0] != -1)&&(p[0]<95))
    if (n[0] != -1)
        return labelsFile.get(n[0]);
    else
        return "Unkown";
}

  IplImage MatToIplImage(Mat m,int width,int heigth)
  {
       Bitmap bmp=Bitmap.createBitmap(m.width(), m.height(), Bitmap.Config.ARGB_8888);


       Utils.matToBitmap(m, bmp);
       return BitmapToIplImage(bmp,width, heigth);

  }
IplImage BitmapToIplImage(Bitmap bmp, int width, int height) {

    if ((width != -1) || (height != -1)) {
        Bitmap bmp2 = Bitmap.createScaledBitmap(bmp, width, height, false);
        bmp = bmp2;
    }
    IplImage image = IplImage.create(bmp.getWidth(), bmp.getHeight(),
            IPL_DEPTH_8U, 4);

    bmp.copyPixelsToBuffer(image.getByteBuffer());

    IplImage grayImg = IplImage.create(image.width(), image.height(),
            IPL_DEPTH_8U, 1);

    cvCvtColor(image, grayImg, opencv_imgproc.CV_BGR2GRAY);

    return grayImg;
}

protected void SaveBmp(Bitmap bmp,String path)
  {
        FileOutputStream file;
        try {
            file = new FileOutputStream(path , true);
        bmp.compress(Bitmap.CompressFormat.JPEG,100,file);  
        file.close();
        }
        catch (Exception e) {
            // TODO Auto-generated catch block
            Log.e("",e.getMessage()+e.getCause());
            e.printStackTrace();
        }
  }

public void load() {
    train();
}
public int getProb() {
    // TODO Auto-generated method stub
    return mProb;
}

}

click to hide/show revision 3
retagged

updated 2014-04-28 01:34:50 -0500

berak gravatar image

unstable face recognition using OpenCV

I've already asked my question in stackoverflow.

I’m developing an android application for face recognition, using JavaCV which is unofficial wrapper of OpenCV. After importing (com.googlecode.javacv.cpp.opencv_contrib.FaceRecognizer) I apply and test the following known methods:

LBPH using createLBPHFaceRecognizer() method
FisherFace using createFisherFaceRecognizer() method
EigenFace using createEigenFaceRecognizer() method

Before I recognize the detected face, I correct the rotated face and crop the proper zone, inspiring from this method

In general when I pass on camera a face already exist in the database, the recognition is ok. But this is not always correct. Sometimes it recognizes the unknown face (not found in Database of trained samples) with a high probability. When we have in the DB two or more faces of similar features (beard, mustache, glasses...) the recognition may be highly mistaken between those faces!

To predict the result using the test face image, I apply the following code:

public String predict(Mat m) {

    int n[] = new int[1];
    double p[] = new double[1];
    IplImage ipl = MatToIplImage(m,WIDTH, HEIGHT);

    faceRecognizer.predict(ipl, n, p);

    if (n[0]!=-1)
     mProb=(int)p[0];
    else
        mProb=-1;
        if (n[0] != -1)
        return labelsFile.get(n[0]);
    else
        return "Unkown";
}

I can’t control the threshold of the probability p, because:

Small p < 50 could predict a correct result.
High p > 70 could predict a false result.
Middle p could predict a correct or false.

As well, I don’t understand why predict() function gives sometime a probability greater than 100 in case of using LBPH??? and in case of Fisher and Eigen it gives very big values (>2000) ?? Can someone help in finding a solution for these bizarre problems? Is there any suggestion to improve robustness of recognition? especially in case of similarity of two different faces.

The following is the entire class using Facerecognizer:

import com.googlecode.javacv.cpp.opencv_imgproc; import com.googlecode.javacv.cpp.opencv_contrib.FaceRecognizer; import com.googlecode.javacv.cpp.opencv_core.IplImage; import com.googlecode.javacv.cpp.opencv_core.MatVector;

import android.graphics.Bitmap; import android.os.Environment; import android.util.Log; import android.widget.Toast;

public class PersonRecognizer {

public final static int MAXIMG = 100;
FaceRecognizer faceRecognizer;
String mPath;
int count=0;
labels labelsFile;

 static  final int WIDTH= 128;
 static  final int HEIGHT= 128;;
 private int mProb=999;


PersonRecognizer(String path)
{
  faceRecognizer =  com.googlecode.javacv.cpp.opencv_contrib.createLBPHFaceRecognizer(2,8,8,8,200);
 // path=Environment.getExternalStorageDirectory()+"/facerecog/faces/";
 mPath=path;
 labelsFile= new labels(mPath);


}

void changeRecognizer(int nRec)
{
    switch(nRec) {
    case 0: faceRecognizer = com.googlecode.javacv.cpp.opencv_contrib.createLBPHFaceRecognizer(1,8,8,8,100);
            break;
    case 1: faceRecognizer = com.googlecode.javacv.cpp.opencv_contrib.createFisherFaceRecognizer();
            break;
    case 2: faceRecognizer = com.googlecode.javacv.cpp.opencv_contrib.createEigenFaceRecognizer();
            break;
    }
    train();

}

void add(Mat m, String description) {
    Bitmap bmp= Bitmap.createBitmap(m.width(), m.height(), Bitmap.Config.ARGB_8888);

    Utils.matToBitmap(m,bmp);
    bmp= Bitmap.createScaledBitmap(bmp, WIDTH, HEIGHT, false);

    FileOutputStream f;
    try {
        f = new FileOutputStream(mPath+description+"-"+count+".jpg",true);
        count++;
        bmp.compress(Bitmap.CompressFormat.JPEG, 100, f);
        f.close();

    } catch (Exception e) {
        Log.e("error",e.getCause()+" "+e.getMessage());
        e.printStackTrace();

    }
}

public boolean train() {

    File root = new File(mPath);
    Log.i("mPath",mPath);
    FilenameFilter pngFilter = new FilenameFilter() {
        public boolean accept(File dir, String name) {
            return name.toLowerCase().endsWith(".jpg");

    };
    };

    File[] imageFiles = root.listFiles(pngFilter);

    MatVector images = new MatVector(imageFiles.length);

    int[] labels = new int[imageFiles.length];

    int counter = 0;
    int label;

    IplImage img=null;
    IplImage grayImg;

    int i1=mPath.length();


    for (File image : imageFiles) {
        String p = image.getAbsolutePath();
        img = cvLoadImage(p);

        if (img==null)
            Log.e("Error","Error cVLoadImage");
        Log.i("image",p);

        int i2=p.lastIndexOf("-");
        int i3=p.lastIndexOf(".");
        int icount=Integer.parseInt(p.substring(i2+1,i3)); 
        if (count<icount) count++;

        String description=p.substring(i1,i2);

        if (labelsFile.get(description)<0)
            labelsFile.add(description, labelsFile.max()+1);

        label = labelsFile.get(description);

        grayImg = IplImage.create(img.width(), img.height(), IPL_DEPTH_8U, 1);

        cvCvtColor(img, grayImg, CV_BGR2GRAY);

        images.put(counter, grayImg);

        labels[counter] = label;

        counter++;
    }
    if (counter>0)
        if (labelsFile.max()>1)
            faceRecognizer.train(images, labels);
    labelsFile.Save();
return true;
}

public boolean canPredict()
{
    if (labelsFile.max()>1)
        return true;
    else
        return false;

}
public String predict(Mat m) {
    if (!canPredict())
        return "";
    int n[] = new int[1];
    double p[] = new double[1];
    IplImage ipl = MatToIplImage(m,WIDTH, HEIGHT);

// IplImage ipl = MatToIplImage(m,-1, -1);

    faceRecognizer.predict(ipl, n, p);

    if (n[0]!=-1)
     mProb=(int)p[0];
    else
        mProb=-1;
//  if ((n[0] != -1)&&(p[0]<95))
    if (n[0] != -1)
        return labelsFile.get(n[0]);
    else
        return "Unkown";
}

  IplImage MatToIplImage(Mat m,int width,int heigth)
  {
       Bitmap bmp=Bitmap.createBitmap(m.width(), m.height(), Bitmap.Config.ARGB_8888);


       Utils.matToBitmap(m, bmp);
       return BitmapToIplImage(bmp,width, heigth);

  }
IplImage BitmapToIplImage(Bitmap bmp, int width, int height) {

    if ((width != -1) || (height != -1)) {
        Bitmap bmp2 = Bitmap.createScaledBitmap(bmp, width, height, false);
        bmp = bmp2;
    }
    IplImage image = IplImage.create(bmp.getWidth(), bmp.getHeight(),
            IPL_DEPTH_8U, 4);

    bmp.copyPixelsToBuffer(image.getByteBuffer());

    IplImage grayImg = IplImage.create(image.width(), image.height(),
            IPL_DEPTH_8U, 1);

    cvCvtColor(image, grayImg, opencv_imgproc.CV_BGR2GRAY);

    return grayImg;
}

protected void SaveBmp(Bitmap bmp,String path)
  {
        FileOutputStream file;
        try {
            file = new FileOutputStream(path , true);
        bmp.compress(Bitmap.CompressFormat.JPEG,100,file);  
        file.close();
        }
        catch (Exception e) {
            // TODO Auto-generated catch block
            Log.e("",e.getMessage()+e.getCause());
            e.printStackTrace();
        }
  }

public void load() {
    train();
}
public int getProb() {
    // TODO Auto-generated method stub
    return mProb;
}

}