Emotions from profile faces
I am currently workin on profile faces. I want to be able to detect faces with side views. Is there a way other than having to use haarcascade_profileface.xml ? This cascade tends to have large false alarm rate. Besides, I want to learn some other technique to deal with it. The Facebook-tag people seems to be very much robust while detecting people with side views. Any algorithm which can achieve such robustness? My ultimate goal is to be able to determine emotions from side viewing faces if at all thats possible.
"ultimate goal is to be able to determine emotions from side viewing faces" - you will need a total different approach for this. cascades (of any kind) are for detection, yours is a classification task.
(i'd rather go and train a facial landmarks detector (like dlib's) on profiles, and use an SVM for classification)
btw, you can handle the false alarm rate by increasing the minNeighbours param.
also, try: https://github.com/nenadmarkus/pico (it's incredibly fast, like 5ms for 640x480)
Nice link!
Does SVM training for facial landmark detection involve annotating the images(marking the images with landmark points) and feeding this data to SVM ? How many images ideally are enough for robust facial landmarking? How would the SVm behave if the images I used to train SVM where exactky half side faced and the testing image is more covered than the images used for training?
@berak, do you know if "pico" is patented?
@Gino i don't know..
the code is copyrighted for sure, but i don't think, there's an actual patent on the idea
@berak, I've tested the face detector and it seems great! I've looked for some information about the algorithm, but I've mainly found links to their own website. Furthermore, incredibly there are only 2 citations reported by google scholar. How did you know about it? Anyway, I think that a self-implementation code of the ideas reported on the paper are not prohibited.
I've just sent an email to him.
@berak: "you'd rather train an automatic landmark detector on images". How can that be achieved?