This step is easy : we reuse the source code of previous step 6  and we add the OpenCV face recognition treatment of step 6 of “Mirror Magic”.

Watch this video to see result (http://www.youtube.com/watch?v=yzYIxNgDZu4).

monalisa2

Source code modification

Except some new #include statements and some global variables, all modification are in the callback function video_buffer_callback
For face recognition, gray pictures are required. Thus, once we get the I420 frame, we don’t need to extract color information. This is a great new, since we saw on last post, that this step takes a lot of cpu !
Make it simple : we forget the pu and pv channels, we only keep the “py” IplImage (gray channel) and convert it to a Mat object.

The face detection is made by the detectMultiScale function. This call requires most of the cpu needed in a loop, it’s important to optimize it.

  • Let’s use the LBP cascade (Local Binaries Patterns) instead of the Haar cascade file (haarcascade_frontalface_alt.xml). Modify the fn_haar variable to link to lbpcascade_frontalface.xml. Response time is much more faster but less accurate. Sometimes, (and you can see exemple on the video), the soft gives wrong predictions
  • Let’s increase the size of minimum rectangle to search size(80,80) instead of size(60,60) as last parameter of the call.
  • I read on the “blog de remi” a way to optimize this function, using an alternative home-made function smartDetect. Unfortunatly, but I didn’t notice any improvment. Thus, I removed it. (perhaps I did a mistake or a misuse ?)

Results

  • With a 320×240 frame, I’m between 8 and 17 FPS with almost no lag (17 FPS = no face to detect and to analyse. 8 FPS = analyse face at each loop)
  • With a 640×480 frame, I’m around 4-5 FPS whith a small lag (1 s)

Conclusions

For me, these results are very good for a such affordable computer like Rasperry Pi. Of course, for a real-time use like RC robot or vehicle it’s too slow (need to detect quickly an obstacle, except if you build a RPCS (Raspberry Pi Controlled Snail) ;-).
But for most of others uses like domotic or education it’s fine.
Anyway, it’s far better than my USB webcam : I was even unable to do face recognition in 640×480 !

Download the source code here (http://raufast.org/download/camcv_vid1.cpp). It’s really a quick&dirty code, don’t be offended by non-respect of C++ state-of-the-art coding rules !

monalisa1

At this stage, you should be able to detect Mona Lisa, in case of she rings at your door tonight 😉