Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
The CMU Pose, Illumination, and Expression (PIE) Database
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Active Appearance Models Revisited
International Journal of Computer Vision
Computer Vision and Image Understanding
Fourier Active Appearance Models
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
| Hi-index | 0.00 |
Illumination and facial pose conditions have an explicit effect on the performance of face recognition systems, caused by the complicated non-linear variation between feature points and views. In this paper, we present a Kernel similarity based Active Appearance Models (KSAAMs) in which we use a Kernel Method to replace Principal Component Analysis (PCA) which is used for feature extraction in Active Appearance Models. The major advantage of the proposed approach lies in a more efficient search of non-linear varied parameter under complex face illumination and pose variation conditions. As a consequence, images illuminated from different directions, and images with variable poses can easily be synthesized by changing the parameters found by KSAAMs. From the experimental results, the proposed method provides higher accuracy than classical Active Appearance Model for face alignment in a point-to-point error sense.