Automatic Interpretation and Coding of Face Images Using Flexible Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Line Edge Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Symmetry as a Continuous Feature
IEEE Transactions on Pattern Analysis and Machine Intelligence
Single Axis Geometry by Fitting Conics
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Pose Invariant Face Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Detecting Symmetry in Grey Level Images: The Global Optimization Approach
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generating frontal view face image for pose invariant face recognition
Pattern Recognition Letters
Pose-Robust Facial Expression Recognition Using View-Based 2D 3D AAM
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
3-D face structure extraction and recognition from images using 3-D morphing and distance mapping
IEEE Transactions on Image Processing
Visual Focus of Attention in Non-calibrated Environments using Gaze Estimation
International Journal of Computer Vision
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This paper proposes a novel approach to pose removal from face images based on the inherent symmetry that is present in faces. In order for face recognition systems and expression classification systems to operate optimally, subjects must look directly into the camera. The removal of pose from face images after their capture removes this restriction. To obtain a pose-removed face image, the frequency components at each position of the face image, obtained through a wavelet transformation, are examined. A cost function based on the symmetry of this wavelet transformed face image is minimized to achieve pose removal. Experimental results are presented that demonstrate that the proposed algorithm improves upon existing techniques in the literature.