Deep Regression for Face Alignment

09/18/2014
by   Baoguang Shi, et al.
0

In this paper, we present a deep regression approach for face alignment. The deep architecture consists of a global layer and multi-stage local layers. We apply the back-propagation algorithm with the dropout strategy to jointly optimize the regression parameters. We show that the resulting deep regressor gradually and evenly approaches the true facial landmarks stage by stage, avoiding the tendency to yield over-strong early stage regressors while over-weak later stage regressors. Experimental results show that our approach achieves the state-of-the-art

READ FULL TEXT

page 2

page 7

research
09/29/2016

Two-stage Convolutional Part Heatmap Regression for the 1st 3D Face Alignment in the Wild (3DFAW) Challenge

This paper describes our submission to the 1st 3D Face Alignment in the ...
research
06/06/2017

Deep Alignment Network: A convolutional neural network for robust face alignment

In this paper, we propose Deep Alignment Network (DAN), a robust face al...
research
11/21/2016

Cascaded Face Alignment via Intimacy Definition Feature

In this paper, we present a random-forest based fast cascaded regression...
research
01/29/2016

Face Alignment by Local Deep Descriptor Regression

We present an algorithm for extracting key-point descriptors using deep ...
research
11/13/2015

Robust Face Alignment Using a Mixture of Invariant Experts

Face alignment, which is the task of finding the locations of a set of f...
research
11/30/2016

Efficient Likelihood Bayesian Constrained Local Model

The constrained local model (CLM) proposes a paradigm that the locations...
research
07/31/2016

Learning deep representation from coarse to fine for face alignment

In this paper, we propose a novel face alignment method that trains deep...

Please sign up or login with your details

Forgot password? Click here to reset