Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
A review on Gabor wavelets for face recognition
Pattern Analysis & Applications
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
MutualBoost learning for selecting Gabor features for face recognition
Pattern Recognition Letters
Gabor wavelets and General Discriminant Analysis for face identification and verification
Image and Vision Computing
Gabor wavelets and General Discriminant Analysis for face identification and verification
Image and Vision Computing
Illumination Invariant Face Recognition Using Near-Infrared Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Fusing continuous spectral images for face recognition under indoor and outdoor illuminants
Machine Vision and Applications
Score normalization in multimodal biometric systems
Pattern Recognition
Directional binary code with application to PolyU near-infrared face database
Pattern Recognition Letters
Nighttime face recognition at long distance: cross-distance and cross-spectral matching
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Hi-index | 0.00 |
Pose and illuminations remain great challenges to current face recognition technique. In this paper, visible image (VI) and near-infrared image (NIR) are fused for performance improvement. When directional binary code is adopted as feature representation, AdaBoost algorithm and the cascade structure are used for classification. Fusion is done at decision level and classification scores are normalized using three different rules, i.e. Min-Max, Z-Score and Tanh-Estimators. Experimental results suggest that the proposed algorithm using VI achieve better performance than NIR when pose and expression variations are present. However, NIR shows much better robustness against illumination and time difference than VI. Due to the complementary information available in two image modalities, fusion of NIR and VI further improves the system performance.