My solution with regards to bad facial detection in OpenCV is to do the following:
1. Use an LBP cascade on the picture. This is lower quality, higher false positives. Uses integer math so this is fast. Its named lbpcascade_frontalface.xml
2. Capture the regions of interest that the LBP cascade identifies with a face and throw into a vector<Mat>. This means you can capture (potentially) arbitrary amount of faces. Of course, with OpenCV you are limited to a minimum of 28px by 28px minimum face.
3. Run the haar cascade for eye detection on the ROI's you saved in the vector. Ones that return eyes show a good match. Haar cascades are slower(because they use floats), but the reduction in pixels means its relatively fast. Its named haarcascade_eye_tree_eyeglasses.xml
I can maintain 20fps with this setup at 800x600 on a slow computer.
1. Use an LBP cascade on the picture. This is lower quality, higher false positives. Uses integer math so this is fast. Its named lbpcascade_frontalface.xml
2. Capture the regions of interest that the LBP cascade identifies with a face and throw into a vector<Mat>. This means you can capture (potentially) arbitrary amount of faces. Of course, with OpenCV you are limited to a minimum of 28px by 28px minimum face.
3. Run the haar cascade for eye detection on the ROI's you saved in the vector. Ones that return eyes show a good match. Haar cascades are slower(because they use floats), but the reduction in pixels means its relatively fast. Its named haarcascade_eye_tree_eyeglasses.xml
I can maintain 20fps with this setup at 800x600 on a slow computer.