Face alignment using boosting and evolutionary search

Authors:
Hua Zhang;Duanduan Liu;Mannes Poel;Anton Nijholt
Affiliations:
College of Software Engineering, Southeast University, Nanjing, China;Lab of Science and Technology, Southeast University, Nanjing, China;Human Media Interaction, University of Twente, Enschede, The Netherlands;Human Media Interaction, University of Twente, Enschede, The Netherlands
Venue:
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Year:
2009

Citing 9
Cited 0

Trainable method of parametric shape description

Image and Vision Computing - Special issue: BMVC 1991
The FERET Evaluation Methodology for Face-Recognition Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Appearance Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolution of Ship Detectors for Satellite SAR Imagery

Proceedings of the Second European Workshop on Genetic Programming
A decision-theoretic generalization of on-line learning and an application to boosting

EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
The CMU Pose, Illumination, and Expression Database

IEEE Transactions on Pattern Analysis and Machine Intelligence
Accurate Face Alignment using Shape Constrained Markov Network

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
High-Performance Rotation Invariant Multiview Face Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian tangent shape model: Estimating shape and pose parameters via bayesian inference

CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present a face alignment approach using granular features, boosting, and an evolutionary search algorithm. Active Appearance Models (AAM) integrate a shape-texture-combined morphable face model into an efficient fitting strategy, then Boosting Appearance Models (BAM) consider the face alignment problem as a process of maximizing the response from a boosting classifier. Enlightened by AAM and BAM, we present a framework which implements improved boosting classifiers based on more discriminative features and exhaustive search strategies. In this paper, we utilize granular features to replace the conventional rectangular Haar-like features, to improve discriminability, computational efficiency, and a larger search space. At the same time, we adopt the evolutionary search process to solve the deficiency of searching in the large feature space. Finally, we test our approach on a series of challenging data sets, to show the accuracy and efficiency on versatile face images.