Recovering the 3D shape and poses of face images based on the similarity transform

Authors:
Hei-Sheung Koo;Kin-Man Lam
Affiliations:
Centre for Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong;Centre for Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong
Venue:
Pattern Recognition Letters
Year:
2008

Citing 12
Cited 2

Motion and Structure from Orthographic Projections

IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape and motion from image streams under orthography: a factorization method

International Journal of Computer Vision
3D motion recovery via affine epipolar geometry

International Journal of Computer Vision
A Sequential Factorization Method for Recovering Shape and Motion From Image Streams

IEEE Transactions on Pattern Analysis and Machine Intelligence
Least Squares Estimation of 3D Shape and Motion of Rigid Objects from Their Orthographic Projections

IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Similarity and Affine Invariant Distances Between 2D Point Sets

IEEE Transactions on Pattern Analysis and Machine Intelligence
An Automatic Construction of a Person's Face Model from the Person's Two Orthogonal Views

GMP '02 Proceedings of the Geometric Modeling and Processing — Theory and Applications (GMP'02)
Face Recognition Using Laplacianfaces

IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient 3D reconstruction for face recognition

Pattern Recognition
Automatic facial feature extraction and 3D face modeling using two orthogonal views with application to 3D face recognition

Pattern Recognition

Recursive Shape and Pose Determination Using Deformable Model

CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Head Pose Determination Using Synthetic Images

ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems

Quantified Score

Hi-index	0.10

Visualization

Abstract

In this paper, a new algorithm is proposed to derive the 3D structure of a human face from a group of face images under different poses. Based on the corresponding 2D feature points of the respective images, their respective poses and the depths of the feature points can be estimated based on measurements using the similarity transform. To accurately estimate the pose of and the 3D information about a human face, the genetic algorithm (GA) is applied. Our algorithm does not require any prior knowledge of camera calibration, and has no limitation on the possible poses or the scale of the face images. It also provides a means to evaluate the accuracy of the constructed 3D face model based on the similarity transform of the 2D feature point sets. Our approach can also be extended to face recognition to alleviate the effect of pose variations. Experimental results show that our proposed algorithm can construct a 3D face structure reliably and efficiently.