Real time head segmentation using opencv

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I am using opencv 2.410 to implement a project. My project allows to segment head from video sequence which get from camera. First, I detect the head region and then apply segmentation method for that ROI region. For high accurate segmentation, I have chosen Grabcut method. However, it is very slow. I only achieved about 2 frames/second (Although I used downsampling method). I have two questions:

1. Have any faster method than Grabcut which have similar accuracy? On other hands, Do we have any way to segment head region.

2. Could you see my code and give me some optimal way to make it faster?

Thank you in advance

    #include <iostream>
    #include <string>
    #include <time.h>
    //include opencv core
    #include "opencv2\core\core.hpp"
    #include "opencv2\contrib\contrib.hpp"
    #include "opencv2\highgui\highgui.hpp"
    #include "opencv2\objdetect\objdetect.hpp"
    #include "opencv2\opencv.hpp"

    //file handling
    #include <fstream>
    #include <sstream>

    using namespace std;
    using namespace cv;

    //Functions
    int VideoDisplay();
    Mat GrabCut(Mat image);

    const unsigned int BORDER = 5;
    const unsigned int BORDER2 = BORDER + BORDER;

    int main()
    {   
        int value=VideoDisplay();
        system("pause");
        return 0;
    }
    Mat GrabCut(Mat image)
    {
        clock_t tStart_all = clock();
        cv::Mat result; // segmentation result (4 possible values)
        cv::Mat bgModel,fgModel; // the models (internally used)
        // downsample the image
        cv::Mat downsampled;
        cv::pyrDown(image, downsampled, cv::Size(image.cols/2, image.rows/2));
        cv::Rect rectangle(BORDER,BORDER,downsampled.cols-BORDER2,downsampled.rows-BORDER2);

        clock_t tStart = clock();
        // GrabCut segmentation
        cv::grabCut(downsampled,    // input image
            result,   // segmentation result
            rectangle,// rectangle containing foreground
            bgModel,fgModel, // models
            1,        // number of iterations
            cv::GC_INIT_WITH_RECT); // use rectangle
        printf("Time taken by GrabCut with downsampled image: %f s\n", (clock() - tStart)/(double)CLOCKS_PER_SEC);

        // Get the pixels marked as likely foreground
        cv::compare(result,cv::GC_PR_FGD,result,cv::CMP_EQ);
        // upsample the resulting mask
        cv::Mat resultUp;
        cv::pyrUp(result, resultUp, cv::Size(result.cols*2, result.rows*2));
        // Generate output image
        cv::Mat foreground(image.size(),CV_8UC3,cv::Scalar(255,255,255));
        image.copyTo(foreground,resultUp); // bg pixels not copied
        return foreground;
    }

    int  VideoDisplay(){

        cout << "start recognizing..." << endl;
        //lbpcascades/lbpcascade_frontalface.xml
        string classifier = "C:/opencv/sources/data/haarcascades/haarcascade_frontalface_default.xml";

        CascadeClassifier face_cascade;
        string window = "Capture - face detection";

        if (!face_cascade.load(classifier)){
            cout << " Error loading file" << endl;
            return -1;
        }
        VideoCapture cap(0);
        //VideoCapture cap("C:/Users/lsf-admin/Pictures/Camera Roll/video000.mp4");

        if (!cap.isOpened())
        {
            cout << "exit" << endl;
            return -1;
        }

        //double fps = cap.get(CV_CAP_PROP_FPS);
        //cout << " Frames per seconds " << fps << endl;
        namedWindow(window, 1);
        long count = 0;
        int fps=0;
        //Start and end times
        time_t start,end;
        //Start the clock
        time(&start);
        int counter=0;



        while (true)
        {
            vector<Rect> faces;
            Mat frame;
            Mat graySacleFrame;
            Mat original;

            cap >> frame;

            time(&end);
            ++counter;
            double sec=difftime(end,start);
            fps=counter/sec;

            if (!frame.empty()){

                //clone from original frame
                original = frame.clone();

                //convert image to gray scale and equalize
                cvtColor(original, graySacleFrame, CV_BGR2GRAY);
                //equalizeHist(graySacleFrame, graySacleFrame);

                //detect face in gray image
                face_cascade.detectMultiScale(graySacleFrame, faces, 1.1, 3, 0, cv::Size(90, 90));

                //number of faces detected
                //cout << faces.size() << " faces ...