6

I am currently using EmguCV (OpenCV C# wrapper) sucessfully to detect faces in real-time (webcam). I get around 7 FPS.

Now I'm looking to improve the performances (and save CPU cycles), and I'm looking for options, here are my ideas:

  • Detect the face, pick up features of the face and try to find those features in the next frames (using SURF algorithm), so this becomes a "face detection + tracking". If not found, use face detection again.

  • Detect the face, in the next frame, try to detect the face in a ROI where the face previously was (i.e. look for the face in a smaller part of the image). If the face is not found, try looking for it in the whole image again.

  • Side idea: if no face detected for 2-3 frames, and no movement in the image, don't try to detect anymore faces until movement is detected.

Do you have any suggestions for me ?

Thanks.

Matthieu Napoli
  • 48,448
  • 45
  • 173
  • 261

3 Answers3

3

  • For the SURF algorithm, you could try, but i am not sure that it provides relevant features on a face, maybe around the eyes, or if you are close and have skin irregularities, or again maybe in the hair if the resolution is enough. Moreover, SURF is not really really fast, and i would just avoiding doing more calculous if you want to save CPU time.

  • The roi is a good idea, you would choose it by doing a camshift algorithm, it won't save a lot of CPU, but you could try as camshift is a very lightweight algorithm. Again i am not sure it will be really relevant, but you got the good idea in your second post : minimize the zone where to search...

  • The side idea seems quite good to me, you could try to detect motion (global motion for instance), if there's not so much, then don't try to detect again what you already detected ... You could try doing that with motion templates as you know the silouhette from meanshift or face detection... A very simple, lightweight but un-robust template matching with the frame n-1 and frame n could give you aswell a coefficient that measures a sort of similarity between these two frames, you can say that below a certain threshold you activate face detection.... why not ? It should take 5min to implement if the C# wrapper has the matchTemplate() equivalent function...

I'll come back here if i have better (deeper) ideas, but for now, i've just come back from work and it's hard to think more...

Julien,

jmartel
  • 2,771
  • 4
  • 24
  • 27
  • Great!! Camshift was what I once saw (coupled with face detection), but I thought it was SURF algorithm. Thank you for that, camshift seems really appropriate, and faster than whole face recognition. Additionnally, it can help me to identify and follow 1 user and keep track of him. – Matthieu Napoli Jul 15 '11 at 21:30
  • I'll look into template matching too, thanks for the idea. If you have anymore coming, I'll be glad to here them. I'll +1 your answer and wait for other suggestions. – Matthieu Napoli Jul 15 '11 at 21:31
  • 1
    Hehe no problem, it's always a pleasure to help another INSA mate, i'll look at some other algorithms, hoping it could help you... – jmartel Jul 16 '11 at 08:26
3

All the solutions you introduced seem to be smart and reasonable. However, if you use Haar for face detection you might try to create a cascade with less stages. Although 20 stages are recommended for face detection, 10-15 might be enough. That would noticeably improve performance. Information on creating own cascades can be found at Tutorial: OpenCV haartraining (Rapid Object Detection With A Cascade of Boosted Classifiers Based on Haar-like Features).

Again, using SURF is a good idea. You can also try P-N learning: Bootstrapping binary classifiers by structural constraints. There are interesting videos on YouTube presenting this method, try to find them.

Jacek
  • 1,894
  • 5
  • 21
  • 34
0

This is not a perfect answer, but just a suggestion.

In my digital image processing classes in my last semester of B.Tech in CS, i learned about bit place slicing, and how the image with just its MSB plane information gives almost 70% of the useful image information. So, you'll be working with almost the original image but with just one-eighth the size of the original.

So although i haven't implemented it in my own project, i was wondering about it, to speed up face detection. Because later on, eye detection, pupil and eye corner detection also take up a lot of computation time and make the whole program slow.

bad_keypoints
  • 1,382
  • 2
  • 23
  • 45