From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lambertian Reflectance and Linear Subspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Fast and Accurate Facial Pose Estimation by Aligning a 3D Appearance Model
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
IEEE Transactions on Pattern Analysis and Machine Intelligence
2D and 3D face recognition: A survey
Pattern Recognition Letters
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Hi-index | 0.00 |
This paper proposes a new method to compare similarities of candidate models that are fitted to different areas of a query image. This method extracts the discriminant features that are changed due to the varying pose/lighting condition of given query image, and the confidence of each model-fitting is evaluated based on how much of the discriminant features is captured in each foreground. The confidence is fused with the similarity to enhance the face-identification performance. In an experiment using 7,000 images of 200 subjects taken under largely varying pose and lighting conditions, our proposed method reduced the recognition errors by more than 25% compared to the conventional method.