Expression-invariant face recognition with accurate optical flow

Authors:
Chao-Kuei Hsieh;Shang-Hong Lai;Yung-Chang Chen
Affiliations:
Dept. of Electrical Engineering, National Tsing Hua University, Taiwan;Dept. of Computer Science, National Tsing Hua University, Taiwan;Dept. of Electrical Engineering, National Tsing Hua University, Taiwan
Venue:
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Year:
2007

Citing 6
Cited 3

Ranking Prior Likelihood Distributions for Bayesian Shape Localization Framework

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Handbook of Face Recognition

Handbook of Face Recognition
A 3D Facial Expression Database For Facial Behavior Research

FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Expression-Invariant Face Recognition with Expression Classification

CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
Accurate optical flow computation under non-uniform brightness variations

Computer Vision and Image Understanding
Recognizing expression variant faces from a single sample image per class

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

Integrated Expression-Invariant Face Recognition with Constrained Optical Flow

PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
2D expression-invariant face recognition with constrained optical flow

ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
An optical flow-based approach to robust face recognition under expression variations

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Face recognition is one of the most intensively studied topics in computer vision and pattern recognition, but few are focused on how to robustly recognize expressional faces with one single training sample per class. In this paper, we modify the regularization-based optical flow algorithm by imposing constraints on some given point correspondences to obtain precise pixel displacements and intensity variations. By using the optical flow computed for the input expressional face with respect to a referenced neutral face, we remove the expression from the face image by elastic image warping to recognize the subject with facial expression. Numerical validations of the proposed method are given, and experimental results show that the proposed method improves the recognition rate significantly.