Java and haarcascade face and mouth detection - mouth as the nose

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Today I begin to test the project which detects a smile in Java and OpenCv. To recognition face and mouth project used haarcascade_frontalface_alt and haarcascade_mcs_mouth But i don't understand why in some reasons project detect nose as a mouth. I have two methods:

private ArrayList<Mat> detectMouth(String filename) {
    int i = 0;
    ArrayList<Mat> mouths = new ArrayList<Mat>();
    // reading image in grayscale from the given path
    image = Highgui.imread(filename, Highgui.CV_LOAD_IMAGE_GRAYSCALE);
    MatOfRect faceDetections = new MatOfRect();
    // detecting face(s) on given image and saving them to MatofRect object
    faceDetector.detectMultiScale(image, faceDetections);
    System.out.println(String.format("Detected %s faces", faceDetections.toArray().length));
    MatOfRect mouthDetections = new MatOfRect();
    // detecting mouth(s) on given image and saving them to MatOfRect object
    mouthDetector.detectMultiScale(image, mouthDetections);
    System.out.println(String.format("Detected %s mouths", mouthDetections.toArray().length));
    for (Rect face : faceDetections.toArray()) {
        Mat outFace = image.submat(face);
        // saving cropped face to picture
        Highgui.imwrite("face" + i + ".png", outFace);
        for (Rect mouth : mouthDetections.toArray()) {
            // trying to find right mouth
            // if the mouth is in the lower 2/5 of the face
            // and the lower edge of mouth is above of the face
            // and the horizontal center of the mouth is the enter of the face
            if (mouth.y > face.y + face.height * 3 / 5 && mouth.y + mouth.height < face.y + face.height
                    && Math.abs((mouth.x + mouth.width / 2)) - (face.x + face.width / 2) < face.width / 10) {
                Mat outMouth = image.submat(mouth);
                // resizing mouth to the unified size of trainSize
                Imgproc.resize(outMouth, outMouth, trainSize);
                mouths.add(outMouth);
                // saving mouth to picture 
                Highgui.imwrite("mouth" + i + ".png", outMouth);
                i++;
            }
        }
    }
    return mouths;
}

and detect smile

private void detectSmile(ArrayList<Mat> mouths) {
        trainSVM();
        CvSVMParams params = new CvSVMParams();
        // set linear kernel (no mapping, regression is done in the original feature space)
        params.set_kernel_type(CvSVM.LINEAR);
    // train SVM with images in trainingImages, labels in trainingLabels, given params with empty samples
        clasificador = new CvSVM(trainingImages, trainingLabels, new Mat(), new Mat(), params);
        // save generated SVM to file, so we can see what it generated
        clasificador.save("svm.xml");
        // loading previously saved file
        clasificador.load("svm.xml");
        // returnin, if there aren't any samples
        if (mouths.isEmpty()) {
            System.out.println("No mouth detected");
            return;
        }
        for (Mat mouth : mouths) {
            Mat out = new Mat();
            // converting to 32 bit floating point in gray scale
            mouth.convertTo(out, CvType.CV_32FC1);
            if (clasificador.predict(out.reshape(1, 1)) == 1.0) {
                System.out.println("Detected happy face");
            } else {
                System.out.println("Detected not a happy face");
            }
        }
    }

training method:

private void train(String flag) {
        String path;
        if (flag.equalsIgnoreCase("positive")) {
            path = trainPath + "smile/";
        } else {
            path = trainPath + "neutral/";
        }
        for (File file : new File(path).listFiles()) {
            Mat img = new Mat();
            Mat con = Highgui.imread(file.getAbsolutePath(), Highgui.CV_LOAD_IMAGE_GRAYSCALE);
            con.convertTo(img, CvType.CV_32FC1, 1.0 / 255.0);
            Imgproc.resize(img, img, trainSize);
            trainingImages.push_back(img.reshape(1, 1));
            if (flag.equalsIgnoreCase("positive")) {
                trainingLabels.push_back(Mat.ones(new Size(1, 1), CvType.CV_32FC1));
            } else {
                trainingLabels.push_back(Mat.zeros(new Size(1, 1), CvType.CV_32FC1));
            }
        }
    }

Examples: For that picture

correctly detects ... (more)