FisherFaceRecognizer- prediction result always 1

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Hi guys, I'm, trying to replicate what posted at: http://docs.opencv.org/trunk/modules/contrib/doc/facerec/tutorial/facerec_video_recognition.html (it's Facial Recognition from Video, using FisherFace algorithm)

I've changed the quoted code only to retrieve vector<mat> images and vector<int> labels from Database. They are input parameters for my function, and I've verified that the right .bmp images are opened What is happening is that I always get result 1 for "prediction" vector..Even if i use different photos with different expressions (of my brother too).

I know it may seem vague, and for that reason I attach the code, but have you any suggestion on what the problem can be? thanks A

#include "F_imageprocessing.h"
#include <iostream>
#include <fstream>
#include <sstream>
#include "opencv/cv.h"
#include "opencv/cvaux.h"
#include "main_derp.h"

#include "opencv2/contrib/contrib.hpp"
//http://docs.opencv.org/trunk/modules/contrib/doc/facerec/tutorial/facerec_video_recognition.html
imageprocessing::imageprocessing(QObject *parent) :
    QObject(parent)
{
}

int imageprocessing::imagecheck(QVector<QString> img_paths, QVector<int> img_ID)
{    
    int prediction = 0;
    //string fn_haar = (QDir::currentPath()+"/haarcascade_frontalface_default.xml").toStdString();
    string fn_haar = "/opt/local/share/OpenCV/haarcascades/haarcascade_frontalface_default.xml";
    vector<Mat> images;
    vector<int> labels;
    for (int j=0; j<img_paths.count(); j++)
    {
        if(!img_paths[j].toStdString().empty())
        {
            QImage q_image;
            q_image.load(QString(img_paths[j]));
            images.push_back(imread(img_paths[j].toStdString().c_str(), 0));
            labels.push_back(img_ID[j]);
        }else return -1;
    }
    int im_width = images[0].cols;
    int im_height = images[0].rows;

    // Get a handle to the Video device:
    VideoCapture cap(0);
    cap.isOpened();
    Mat frame;

    //start of Algorithm (fisherface)
    Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
    //Ptr<FaceRecognizer> model = createEigenFaceRecognizer();
    model->train(images, labels);
    CascadeClassifier haar_cascade;
    haar_cascade.load(fn_haar);
    for(int zz=0;zz<3;zz++) {
    //for(;;) {
        cap >> frame;
        // Clone the current frame:
        Mat original = frame.clone();
        // Convert the current frame to grayscale:
        Mat gray;
        cvtColor(original, gray, CV_BGR2GRAY);
        // Find the faces in the frame:
        vector< Rect_<int> > faces;
        haar_cascade.detectMultiScale(gray, faces);
        for(int i = 0; i < faces.size(); i++) {
            Rect face_i = faces[i];
            Mat face = gray(face_i);
            Mat face_resized;
            cv::resize(face, face_resized, Size(im_width, im_height), 1.0, 1.0, INTER_CUBIC);
            prediction = model->predict(face_resized);
            qDebug()<<prediction;
            /*create rectangle around detected image
            rectangle(original, face_i, CV_RGB(0, 255,0), 1);
            string box_text = format("Prediction = %d", prediction);
            // Calculate the position for annotated text and put the rectangle into image
            int pos_x = std::max(face_i.tl().x - 10, 0);
            int pos_y = std::max(face_i.tl().y - 10, 0);
            putText(original, box_text, Point(pos_x, pos_y), FONT_HERSHEY_PLAIN, 1.0, CV_RGB(0,255,0), 2.0);*/

        }

        /*Show the result:
        imshow("face_recognizer", original);
        char key = (char) waitKey(20);*/
        // Exit this loop on escape:
    }
    qDebug()<<prediction;
    return prediction;
}