I have a not-so-simple question.
The Situation:
I'm working on robust facial detection API in python written on top of OpenCV (cv not cv2).
I am using Haar Cascades for face detection specially
front - haarcascade_frontalface_default.xml
profile - haarcascade_profileface.xml
Each worker is using different harr classifier (front/profile) and produce the set of ROI (Region of Interests) then do a uion on them and merge all overlaping bouding boxes.
The result is "your casual red square" around a face with 70% accuracy and not so may phantom faces.
The problem:
Simply tilting the face. My algorithm cannot detect a tilted face.
For profile detection I did a simple flip of a image to detect both left and right profile.
I was thinking there "should" be a better way to detect a tilted face than to call algorithm multiple times for multiple slightly rotated images. (This is a only solution that came to my mind).
The question:
Is there a approach or a way or a specific harr classifier for detection of tilted faces?
Thank you :)
