OpenCVFaceRecognizer.java

import java.io.File;
import java.io.FilenameFilter;
import java.nio.IntBuffer;

import static org.bytedeco.javacpp.opencv_core.CV_32SC1;
import static org.bytedeco.javacpp.opencv_core.CV_8UC1;
import static org.bytedeco.javacpp.opencv_face.createFisherFaceRecognizer;
// import static org.bytedeco.javacpp.opencv_face.createEigenFaceRecognizer;
// import static org.bytedeco.javacpp.opencv_face.createLBPHFaceRecognizer;
import static org.bytedeco.javacpp.opencv_imgcodecs.imread;
import static org.bytedeco.javacpp.opencv_imgcodecs.CV_LOAD_IMAGE_GRAYSCALE;

import org.bytedeco.javacpp.BytePointer;
import org.bytedeco.javacpp.IntPointer;
import org.bytedeco.javacpp.DoublePointer;
import org.bytedeco.javacpp.opencv_face.FaceRecognizer;
import org.bytedeco.javacpp.opencv_core.Mat;
import org.bytedeco.javacpp.opencv_core.MatVector;

/**
 * I couldn't find any tutorial on how to perform face recognition using OpenCV and Java,
 * so I decided to share a viable solution here. The solution is very inefficient in its
 * current form as the training model is built at each run, however it shows what's needed
 * to make it work.
 *
 * The class below takes two arguments: The path to the directory containing the training
 * faces and the path to the image you want to classify. Not that all images has to be of
 * the same size and that the faces already has to be cropped out of their original images
 * (Take a look here http://fivedots.coe.psu.ac.th/~ad/jg/nui07/index.html if you haven't
 * done the face detection yet).
 *
 * For the simplicity of this post, the class also requires that the training images have
 * filename format: <label>-rest_of_filename.png. For example:
 *
 * 1-jon_doe_1.png
 * 1-jon_doe_2.png
 * 2-jane_doe_1.png
 * 2-jane_doe_2.png
 * ...and so on.
 *
 * Source: http://pcbje.com/2012/12/doing-face-recognition-with-javacv/
 *
 * @author Petter Christian Bjelland
 */
public class OpenCVFaceRecognizer {
    public static void main(String[] args) {
        String trainingDir = args[0];
        Mat testImage = imread(args[1], CV_LOAD_IMAGE_GRAYSCALE);

        File root = new File(trainingDir);

        FilenameFilter imgFilter = new FilenameFilter() {
            public boolean accept(File dir, String name) {
                name = name.toLowerCase();
                return name.endsWith(".jpg") || name.endsWith(".pgm") || name.endsWith(".png");
            }
        };

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

        MatVector images = new MatVector(imageFiles.length);

        Mat labels = new Mat(imageFiles.length, 1, CV_32SC1);
        IntBuffer labelsBuf = labels.createBuffer();

        int counter = 0;

        for (File image : imageFiles) {
            Mat img = imread(image.getAbsolutePath(), CV_LOAD_IMAGE_GRAYSCALE);

            int label = Integer.parseInt(image.getName().split("\\-")[0]);

            images.put(counter, img);

            labelsBuf.put(counter, label);

            counter++;
        }

        FaceRecognizer faceRecognizer = createFisherFaceRecognizer();
        // FaceRecognizer faceRecognizer = createEigenFaceRecognizer();
        // FaceRecognizer faceRecognizer = createLBPHFaceRecognizer()

        faceRecognizer.train(images, labels);

        IntPointer label = new IntPointer(1);
        DoublePointer confidence = new DoublePointer(1);
        faceRecognizer.predict(testImage, label, confidence);
        int predictedLabel = label.get(0);

        System.out.println("Predicted label: " + predictedLabel);
    }
}