Face Alignment Using Cascade Gaussian Process Regression Trees

Donghoon Lee, Hyunsin Park, Chang D. Yoo; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 4204-4212

Abstract


In this paper, we propose a face alignment method that uses cascade Gaussian process regression trees (cGPRT) constructed by combining Gaussian process regression 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 inputs as the number of trees where the two inputs fall in the same leaves. Without increasing prediction time, the prediction of cGPRT can be performed in the same framework as the cascade regression trees (CRT) but with better generalization. Features for GPRT are designed using shape-indexed difference of Gaussian (DoG) filter responses sampled 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.

Related Material


[pdf]
[bibtex]
@InProceedings{Lee_2015_CVPR,
author = {Lee, Donghoon and Park, Hyunsin and Yoo, Chang D.},
title = {Face Alignment Using Cascade Gaussian Process Regression Trees},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}