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Sieving Regression Forest Votes for Facial Feature Detection in the Wild
| Citations: | 14 - 3 self |
BibTeX
@MISC{Yang_sievingregression,
author = {Heng Yang and Ioannis Patras},
title = {Sieving Regression Forest Votes for Facial Feature Detection in the Wild},
year = {}
}
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Abstract
In this paper we propose a method for the localization of multiple facial features on challenging face images. In the regression forests (RF) framework, observations (patches) that are extracted at several image locations cast votes for the localization of several facial features. In order to fil-ter out votes that are not relevant, we pass them through two types of sieves, that are organised in a cascade, and which enforce geometric constraints. The first sieve filters out votes that are not consistent with a hypothesis for the location of the face center. Several sieves of the second type, one associated with each individual facial point, fil-ter out distant votes. We propose a method that adjusts on-the-fly the proximity threshold of each second type sieve by applying a classifier which, based on middle-level features extracted from voting maps for the facial feature in ques-tion, makes a sequence of decisions on whether the thresh-old should be reduced or not. We validate our proposed method on two challenging datasets with images collected from the Internet in which we obtain state of the art results without resorting to explicit facial shape models. We also show the benefits of our method for proximity threshold ad-justment especially on ’difficult ’ face images. 1.
Keyphrases
regression forest facial feature detection middle-level feature several facial feature face image facial shape model proximity threshold art result first sieve filter several image location several sieve multiple facial feature difficult face image second type facial feature face center second type sieve geometric constraint distant vote individual facial point
