CODEWITHSUNDEEP
c++opencvcomputer-visionface-detection
powder
powder

Reputation: 1173

Pose independent face detection

I'm working on a project where I need to detect faces in very messy videos (recorded from an egocentric point of view, so you can imagine..). Faces can have angles of yaw that variate between -90 and +90, pitch with almost the same variation (well, a bit lower due to the human body constraints..) and possibly some roll variations too.

I've spent a lot of time searching for some pose independent face detector. In my project I'm using OpenCV but OpenCV face detector is not even close to the detection rate I need. It has very good results on frontal faces but almost zero results on profile faces. Using haarcascade .xml files trained on profile images doesn't really help. Combining frontal and profile cascades yield slightly better results but still, not even close to what I need.

Training my own haarcascade will be my very last resource since the huge computational (or time) requirements.

By now, what I'm asking is any help or any advice regarding this matter. The requirements for a face detector I could use are:

Real time is not an issue by now, detection rate is everything I care right now.

I've seen many papers achieving these results but i couldn't find any code that I could use.

I sincerely thank for any help that you'll be able to provide.

Upvotes: 1

Views: 1100

Answers (1)

Zaw Lin
Zaw Lin

Reputation: 5708

perhaps not an answer but too long to put into comment.

you can use opencv_traincascade.exe to train a new detector that can detect a wider variety of poses. this post may be of help. http://note.sonots.com/SciSoftware/haartraining.html. i have managed to trained a detector that is sensitive within -50:+50 yaw by using feret data set. for my case, we did not want to detect purely side faces so training data is prepared accordingly. since feret already provides convenient pose variations it might be possible to train a detector somewhat close to your specification. time is not an issue if you are using lbp features, training completes in 4-5 hours at most and it goes even faster(15-30min) by setting appropriate parameters and using fewer training data(useful for ascertaining whether the detector is going to produce the output you expected).

Upvotes: 1

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