Real time face detection in MATLAB - matlab

Question

I'm trying to make a real time face detector using MATLAB. I found a sample code on the Mathworks' page, but it uses a sample video. What I'm having a problem with it that this code only can track the one it chooses to even with a few faces in the opening frame. I need it to track several faces at once. Is that possible with a change in this code that is not drastic.
I found the following code on MathWorks' web page:
% Create a cascade detector object.
faceDetector = vision.CascadeObjectDetector();
% Read a video frame and run the detector.
videoFileReader = vision.VideoFileReader('visionface.avi');
videoFrame = step(videoFileReader);
bbox = step(faceDetector, videoFrame);
% Draw the returned bounding box around the detected face.
videoOut = insertObjectAnnotation(videoFrame,'rectangle',bbox,'Face');
figure, imshow(videoOut), title('Detected face');
% Get the skin tone information by extracting the Hue from the video frame
% converted to the HSV color space.
[hueChannel,~,~] = rgb2hsv(videoFrame);
% Display the Hue Channel data and draw the bounding box around the face.
figure, imshow(hueChannel), title('Hue channel data');
rectangle('Position',bbox(1,:),'LineWidth',2,'EdgeColor',[1 1 0])
% Detect the nose within the face region. The nose provides a more accurate
% measure of the skin tone because it does not contain any background
% pixels.
noseDetector = vision.CascadeObjectDetector('Nose');
faceImage = imcrop(videoFrame,bbox(1,:));
noseBBox = step(noseDetector,faceImage);
% The nose bounding box is defined relative to the cropped face image.
% Adjust the nose bounding box so that it is relative to the original video
% frame.
noseBBox(1,1:2) = noseBBox(1,1:2) + bbox(1,1:2);
% Create a tracker object.
tracker = vision.HistogramBasedTracker;
% Initialize the tracker histogram using the Hue channel pixels from the
% nose.
initializeObject(tracker, hueChannel, noseBBox(1,:));
% Create a video player object for displaying video frames.
videoInfo = info(videoFileReader);
videoPlayer = vision.VideoPlayer('Position',[300 300 videoInfo.VideoSize+30]);
% Track the face over successive video frames until the video is finished.
while ~isDone(videoFileReader)
% Extract the next video frame
videoFrame = step(videoFileReader);
% RGB -> HSV
[hueChannel,~,~] = rgb2hsv(videoFrame);
% Track using the Hue channel data
bbox = step(tracker, hueChannel);
% Insert a bounding box around the object being tracked
videoOut = insertObjectAnnotation(videoFrame,'rectangle',bbox,'Face');
% Display the annotated video frame using the video player object
step(videoPlayer, videoOut);
end
% Release resources
release(videoFileReader);
release(videoPlayer);
Thanks in advance!

Answer 1

That example is designed to only track a single face. For tracking multiple objects please take a look at this example, that uses vision.KalmanFilter objects for tracking. You can replace the detection part in this example with the code to detect faces.
Alternatively, take a look at this example that uses the KLT algorithm (vision.PointTracker) to track points. You can modify that to track multiple faces too, but that is considerably more work. You would have to do a lot of bookkeeping to keep track of which points belong to which face.
Edit:
Here is an example of how to use vision.PointTracker to track multiple faces.