  In this paper, we present a random-forest based fast cascaded regression
model for face alignment, via a novel local feature. Our proposed local
lightweight feature, namely intimacy definition feature (IDF), is more
discriminative than landmark pose-indexed feature, more efficient than
histogram of oriented gradients (HOG) feature and scale-invariant feature
transform (SIFT) feature, and more compact than the local binary feature (LBF).
Experimental results show that our approach achieves state-of-the-art
performance when tested on the most challenging datasets. Compared with an
LBF-based algorithm, our method can achieve about two times the speed-up and
more than 20% improvement, in terms of alignment accuracy measurement, and save
an order of magnitude of memory requirement.
