I can't say this is the best way, but I'm currently apply this to crop the face images, removing the background and hair part as shown in the picture below:
This my reference image.
What I did is I crop (manually, using photo editor) 1 face image as a reference. Then, I apply feature point detection on the face and make 6 points as reference points (I'm using the points on eyes). Doing the same for other face images that you want to process to get the 6 points. Lastly, perform/calculate the geometric transformation as the code below and perform image warp.
[tform,inlierPtsDistorted,inlierPtsOriginal] = estimateGeometricTransform(matchedPtsDistorted,matchedPtsOriginal,'similarity');
outputView = imref2d(size(ori));
Ir = imwarp(img,tform,'OutputView',outputView);
This image shows points matching between reference image and probe image.
The result is as follow:
Regarding the illumination issue, histogram equalization or Retinex able to solve it. There are a lot of algorithm for illumination normalization.