题目：Cascaded face alignment via intimacy definition feature
报告人：Prof. Kenneth K.M. Lam
Department of Electronic and Information Engineering
The Hong Kong Polytechnic University Hong Kong
Recent years have witnessed the emerging popularity of regression-based face aligners, which directly learn mappings between facial appearance and shape-increment manifolds. In this talk, I will introduce a random-forest-based, cascaded regression model for face alignment by using a novel locally lightweight feature, namely intimacy definition feature (IDF). This feature is more discriminative than the pose-indexed feature, more efficient than the histogram of oriented gradients (HOG) feature and the scale-invariant feature transform (SIFT) feature, and more compact than the local binary feature (LBF). Experimental validation of the algorithm shows that it achieves state-of-the-art performance when testing on some challenging datasets. Compared with the LBF-based algorithm, the method achieves about twice the speed, 20% improvement in terms of alignment accuracy, and save an order of magnitude on memory requirement.