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You may want to check out the face detection approach described in this paper: Joint Cascade Face Detection and Alignment. ECCV 2014(http://www.jiansun.org/papers/ECCV14_JointCascade.pdf)

I'd also encourage you to try out the Face API from Microsoft (full disclosure, I work on it). One of the focuses is on improving detection when challenging lighting and occlusions are present: https://www.microsoft.com/cognitive-services/en-us/face-api

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Very interesting paper. Thank you for sharing it.

I had a question after reading it. The parameter rho, that is used to either classify/regress depending on the t cascade stage{1..T}, is said to be set empirically. This, I would assume, could change across data sets, how were you able to decide when classification switched to regression in your tree and how was this adapted when testing on other data?

Also forgot to attach the paper I was referring to: http://arxiv.org/pdf/1502.02766v3.pdf

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