我正在开发一个用于人脸识别的Android应用程序，使用JavaCV这是OpenCV的非官方包装。导入com.googlecode.javacv.cpp.opencv_contrib.FaceRecognizer后，
我申请并测试以下已知方法：
在识别检测到的脸部之前，我会纠正旋转的脸部并剪裁正确的区域，从this method
启发一般情况下，当我传递相机时，数据库中已经存在一个面，识别就可以了。但这并不总是正确的。有时它很有可能识别未知面部（在训练样本的数据库中找不到）。当我们在数据库中有两个或更多相似特征的面孔（胡须，小胡子，眼镜......）时，这些面孔之间的识别可能会非常错误！
要使用测试面部图像预测结果，请应用以下代码：
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";
}
我无法控制概率p的阈值，因为：
同样，我不明白为什么在使用LBPH的情况下，为什么predict（）函数有时会出现大于100的概率？在Fisher和Eigen的情况下，它给出了非常大的值（> 2000）？ 有人可以帮助找到这些奇怪问题的解决方案吗？ 是否有任何建议可以提高认可的稳健性？特别是在两个不同面孔相似的情况下。
以下是使用Facerecognizer的整个班级：
package org.opencv.javacv.facerecognition;
import static com.googlecode.javacv.cpp.opencv_highgui.*;
import static com.googlecode.javacv.cpp.opencv_core.*;
import static com.googlecode.javacv.cpp.opencv_imgproc.*;
import static com.googlecode.javacv.cpp.opencv_contrib.*;
import java.io.File;
import java.io.FileOutputStream;
import java.io.FilenameFilter;
import java.util.ArrayList;
import org.opencv.android.Utils;
import org.opencv.core.Mat;
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;
}
}
答案 0 :(得分：2)
我认为你需要实现一些对光照变化更加健壮的东西。见：Illumination normalization in OpenCV
然后，为了管理图像之间的相似性，您可以使用诸如主成分分析之类的东西。