Facial model fitting based on perturbation learning and it's evaluation on challenging real-world diversities images

Authors:
Koichi Kinoshita;Yoshinori Konishi;Masato Kawade;Hiroshi Murase
Affiliations:
Technology and Intellectual Property HQ, Omron Corporation, Japan,Graduate School of Information Science, Nagoya University, Japan;Technology and Intellectual Property HQ, Omron Corporation, Japan;Technology and Intellectual Property HQ, Omron Corporation, Japan;Graduate School of Information Science, Nagoya University, Japan
Venue:
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Year:
2012

Citing 12
Cited 0

Active shape models—their training and application

Computer Vision and Image Understanding
Face Recognition by Elastic Bunch Graph Matching

IEEE Transactions on Pattern Analysis and Machine Intelligence
A morphable model for the synthesis of 3D faces

Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Retinal vision applied to facial features detection and face authentication

Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Active Appearance Models

ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Robust Face Detection Using the Hausdorff Distance

AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Face Recognition Based on Fitting a 3D Morphable Model

IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Appearance Models Revisited

International Journal of Computer Vision
Acquiring Linear Subspaces for Face Recognition under Variable Lighting

IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Parameter Optimization for AdaBoosted Active Shape Model

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Fast Active Appearance Model Search Using Canonical Correlation Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Feature Models

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a robust and efficient framework for facial shape model fitting. Traditional model fitting approaches are sensitive to noise resulting from scene variations due to lighting, facial expressions, poses, etc., and tend to spend substantial computational effort due to heuristic searching algorithms. Our work distinguishes itself from conventional approaches by employing (a) non-uniform sampling features unified by the shape model that affords robustness, and (b) regression analysis between observed features and underlying shape parameters that allow for efficient model update. We demonstrate the effectiveness of our framework by evaluating its performance on several new and existing datasets including challenging real-world diversities. Significantly higher localization accuracy and speedup factors of 15 have been observed comparing with the traditional approach.