My project is Face Authentication.
System Description: My input is only one image (which was taken when the user logins for the first time) and using that image system should authenticate whenever the user logins to the application. The authentication images may differ from the first input image like -- different illumination conditions, different distance from camera and -10 to 10 degrees variation in pose. The camera used is same (ex: ipad) for all cases.
1) Authentication images are stored each time the user logins. How to
make use of these images to enhance the accuracy of the system??
2) When a new image comes, I need to select the closest image(s) (and
not all stored images) from the image repository and use for
authenticate to reduce the time. How to label an image based on
illumination/distance from camera automatically??
3) How should I make my system to perform decently for changes in
illumination and distance from camera??
Please, can anyone suggest me good alogirthm/papers/opensource-codes for my above questions??
Though it sounds like a research project, I would be extremely grateful if I get any response from someone.
For this task I think you should take a look at OpenCV's Face Recognition API. The API is basically able to identify the structure of a face (within certain limitations of course) and provide you with the coordinates of the image within which the face is available.
Having to deal with just the face in my opinion reduces the need to deal with different background colours which I think is something you do not really need.
Once you have the image of the face, you could scale it up/down to have a uniform size and also change the colour of the image to grey scale. Lastly, I would consider feeding all this information to an Artificial Neural Network since these are able to deal with inconsistencies with the input. This will allow you to increase your knowledge base each time a user logs in.
I'm pretty sure there are other ways to go around this. I would recommend taking a look at Google Scholar to try and find papers which deal with this matter for more information and quite possible other ways to achieve what you are after. Also, keep in mind that with some luck you might also find some open source project which already does most of what you are after.
If you really have a database of photographs of faces, you could probably use that to enhance the features of OpenCV face detection. The way faces are recognized is by comparing the principal components of the picture with those of the face examples in OpenCV database.
Check out:
How to create Haar Cascade (xml) for using with OpenCV?
Seeing that, you could also try to do your own Principal Component Analysis on every picture of a recognized face (use OpenCV face detection for that-> Black out everything exept the face, OpenCV gives you the position and size of the face). Compare the PCA to the ones in your database and match it to the closest. Course, this would work best with a fairly big database, so maybe at the beginning there could be wrong matches.
I think creating your own OpenCV haarcascade would be the best way to go.
Good Luck!
