Chapter 8. Finding faces

OpenIMAJ contains a set of classes that contain implementations of some of the state-of-the-art face detection and recognition algorithms. These classes are provided as a sub-project of the OpenIMAJ code-base called faces. The OpenIMAJ maven archetype adds the face library as a dependency and so we can start building face detection applications straight away.

Create a new application using the quick-start archetype (see tutorial 1) and import it into your IDE. If you look at the pom.xml file you will see that the faces dependency from OpenIMAJ is already included. As you’ve already done the video-processing tutorial, we’ll try to find faces within the video that your cam produces. If you don’t have a cam, follow the video tutorial on how to use video from a file instead.

Start by removing the code from the main method of the class file. Then create a video capture object and a display to show the video. Create a listener on the video display to which we can hook our face finder. The code is below, but check out the previous tutorial on video processing if you’re not sure what it means.

VideoCapture vc = new VideoCapture( 320, 240 );
VideoDisplay<MBFImage> vd = VideoDisplay.createVideoDisplay( vc );
  new VideoDisplayListener<MBFImage>() {
    public void beforeUpdate( MBFImage frame ) {

    public void afterUpdate( VideoDisplay<MBFImage> display ) {

For finding faces in images (or in this case video frames) we use a face detector. The FaceDetector interface provides the API for face detectors and there are currently two implementations within OpenIMAJ - the HaarCascadeDetector and the SandeepFaceDetector. The HaarCascadeDetector is considerably more robust than the SandeepFaceDetector, so we’ll use that.

In the beforeUpdate() method, instantiate a new HaarCascadeDetector. The constructor takes the minimum size in pixels that a face can be detected at. For now, set this to 40 pixels:

FaceDetector<DetectedFace,FImage> fd = new HaarCascadeDetector(40);

Like all FaceDetector implementations, the HaarCascadeDetector has a method detectFaces() which takes an image. Because the HaarCascadeDetector uses single band images, we must convert our multi-band colour image into a single band image. To do this we can use the Transforms utility class that contains some static methods for converting images. The calculateIntensity() method will do just fine. Note that functionally the calculateIntensity() method does the same thing as the flatten() method we used earlier when used on RGB images.

List<DetectedFace> faces = fd.detectFaces(Transforms.calculateIntensity(frame));

The detectFaces() method returns a list of DetectedFace objects which contain information about the faces in the image. From these objects we can get the rectangular bounding boxes of each face and draw them back into our video frame. As we’re doing all this in our beforeUpdate() method, the video display will end up showing the bounding boxes on the displayed video. If you run the code and you have a cam attached, you should see yourself with a box drawn around your face. The complete code is shown below:

FaceDetector<DetectedFace,FImage> fd = new HaarCascadeDetector(40);
List<DetectedFace> faces = fd.detectFaces( Transforms.calculateIntensity(frame));

for( DetectedFace face : faces ) {
    frame.drawShape(face.getBounds(), RGBColour.RED);

OpenIMAJ has other face detectors which go a bit further than just finding the face. The FKEFaceDetector finds facial keypoints (the corners of the eyes, nose and mouth) and we can use this detector instead simply by instantiating that object instead of the HaarCascadeDetector. The FKEFaceDetector returns a slightly different object for each detected face, called a KEDetectedFace. The KEDetectedFace object contains the extra information about where the keypoints in the face are located. The lines of our code to instantiate the detector and detect faces can now be changed to the following:

FaceDetector<KEDetectedFace,FImage> fd = new FKEFaceDetector();
List<KEDetectedFace> faces = fd.detectFaces( Transforms.calculateIntensity( frame ) );

If you run the demo now, you will see exactly the same as before, as the FKEFaceDetector still detects bounding boxes. It may be running a bit slower though, as there is much more processing going on - we’re just not seeing the output of it! So, let’s plot the facial keypoints.

To get the keypoints use getKeypoints() on the detected face. Each keypoint has a position (public field) which is relative to the face, so we’ll need to translate the point to the position of the face within the video frame before we plot the points. To do that we can use the translate() method of the Point2d class and the minX() and minY() methods of the Rectangle class.

8.1. Exercises

8.1.1. Exercise 1: Drawing facial keypoints

Use the information above to plot the facial keypoints on the video.

8.1.2. Exercise 2: Speech bubbles

Try and take the speech bubble from the previous image tutorial and make it come from the mouth in the video. Hints: use getKeypoint(FacialKeypointType) to get the keypoint of the left corner of the mouth and plot the ellipses depending on that point. You may need to use smaller ellipses and text if your video is running at 320x240.