Automatic pose-normalized 3d face modeling and recognition systems

Authors:
Sunjin Yu;Kwontaeg Choi;Sangyoun Lee
Affiliations:
Dept. of Electrical and Electronics Engineering, Biometrics Engineering Research Center, Seoul, South Korea;Dept. of Computer Science, Yonsei University, Biometrics Engineering Research Center, Seoul, South Korea;Dept. of Electrical and Electronics Engineering, Biometrics Engineering Research Center, Seoul, South Korea
Venue:
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Year:
2006

Citing 11
Cited 0

Color-Encoded Structured Light for Rapid Active Ranging

IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Matching with Nonlinear Diffusion

International Journal of Computer Vision
Multiple view geometry in computer visiond

Multiple view geometry in computer visiond
Active Appearance Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Mathematics for 3D game programming and computer graphics

Mathematics for 3D game programming and computer graphics
Introductory Techniques for 3-D Computer Vision

Introductory Techniques for 3-D Computer Vision
Digital Image Processing

Digital Image Processing
In defence of the 8-point algorithm

ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Multi-Modal 2D and 3D Biometrics for Face Recognition

AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Robust Real-Time Face Detection

International Journal of Computer Vision
Robust face recognition using 2D and 3D data: Pose and illumination compensation

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Pose-variation factors present a big problem in 2D face recognition. To solve this problem, we designed a 3D face acquisition system which was able to generate multi-view images. However, this created another pose-estimation problem in terms of normalizing the 3D face data. This paper presents an automatic pose-normalized 3D face data acquisition method that is able to perform both 3D face modeling and 3D face pose-normalization at once. The proposed method uses 2D information with the AAM (Active Appearance Model) and 3D information with a 3D normal vector. The proposed system is based on stereo vision and a structured light system which consists of 2 cameras and 1 projector. In orsder to verify the performance of the proposed method, we designed an experiment for 2.5D face recognition. Experimental results showed that the proposed method is robust against pose variation.