Cascaded Face Alignment via Intimacy Definition Feature

In this paper, we present a fast cascaded regression for face alignment, via a novel local feature. Our proposed local lightweight feature, namely intimacy definition feature (IDF), is more discriminative than landmark shape-indexed feature, more efficient than the handcrafted 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 benchmarks. Compared with an LBF-based algorithm, our method is able to obtain about two times the speed-up and more than 20% improvement, in terms of alignment error measurement, and able to save an order of magnitude of memory requirement. Keywords— Cascaded Face Alignment; Random Forest; Intimacy Definition Feature;