Cascaded Face Alignment via Intimacy Definition Feature
This work addresses face alignment for computer vision applications, presenting an incremental improvement in efficiency and accuracy.
The paper tackles face alignment by proposing a novel local feature called intimacy definition feature (IDF) within a random-forest based cascaded regression model, achieving state-of-the-art performance with about two times speed-up, over 20% accuracy improvement, and an order of magnitude memory savings compared to an LBF-based method.
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.