| I'm doing a bit of tricks to speed up the performance. And I'm perfectly find that you question the performance :) I encourage you to try it out. My first problem/observation is that Haar cascades looove running on a GPU due to their Float-y nature. But dealing with them on a CPU frankly stinks. I was getting 1 frame/10 seconds at 800x600 with the included Haar face detector. That's effectively unusable. Turns out there's also LBP cascades, which are integer based. And they run fast on a cpu. But, from my observations, they have many false positives. But they seem to have no issue with false negatives, so I grab all the faces, plus a few "junks". The speedup is, is that I can use an LBP and then throw the region of interest (the potential face) onto a Haar cascade eye detector. Now that I'm dealing with much smaller pictures, haar runs acceptably. Literally, if
(eyes.size > 0){is valid face.....} Then I proceed to use the built in function on OpenCV contrib Face library. The problems with the library are numerous. Mainly, the settings are provided without good descriptions, or whatever the defaults of whatever academic papers had them set as. Because I'm also an academic, I was able to get ahold of quite a few large datasets of face data. After doing so, I wrote a few small programs that attempted to calculate the ideal settings for the FaceRecognizer call, which I believe I did so. (The settings are in the call, in the source.) Of course, I do get some slowdowns depending on how many faces there are (mainly, stay away from google searches for faces). But then again, 4 haars on 50x50 images is not that bad at all. My machines used: Thinkpad T61 (8GB ram), Intel NUC (8GB ram, I5 cpu)
Camera: Logitech C920 webcam I did try my code using the max resolution the camera could acquire (1920x1080).... 1 frame/5 seconds. |