Face Alignment using Cascade Gaussian Process Regression Trees

In this paper, we propose a face alignment method that uses cascade Gaussian process regression trees (cG-PRT) constructed by combining Gaussian process regres-sion trees (GPRT) in a cascade stage-wise manner. Here, GPRT is a Gaussian process with a kernel defined by a set of trees. The kernel measures the similarity between two in-puts as the number of trees where the two inputs fall in the same leaves. Without increasing prediction time, the pre-diction of cGPRT can be performed in the same framework as the cascade regression trees (CRT) but with better gen-eralization. Features for GPRT are designed using shape-indexed difference of Gaussian (DoG) filter responses sam-pled from local retinal patterns to increase stability and to attain robustness against geometric variances. Compared with the previous CRT-based face alignment methods that have shown state-of-the-art performances, cGPRT using shape-indexed DoG features performed best on the HELEN and 300-W datasets which are the most challenging dataset today. 1