2015-12-08 02:42:25 -0600 | asked a question | How to detect low resolution faces (small faces) in an image using opencv python I'm currently working on a project where I need to analyze thousands of medium-resolution photos, detect visible faces and blur them out. I've read quite a bit about OpenCV (and spent a day building and playing around with it), and I feel that it's the right direction to solve this problem. Currently, I have gotten pretty far along with OpenCV, to the point where I can process a photo, pass it to the detectMultiScale() function and use one of the provided Haar cascade files to detect a good number of faces. However, the hit rate is still pretty abysmal no matter how I play around with the function parameters, or which cascade file I use (frontalface_default vs. frontalface_alt vs. frontalface_alt_tree). I'm perfectly fine with some false positives and false negatives, but I'm nowhere near the rate I'd call acceptable. :I just copied the details from another question because i have same problem |