Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Design of Advanced Correlation Filters for Robust Distortion-Tolerant Face Recognition
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Incremental updating of advanced correlation filters for biometric authentication systems
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
Reliable face recognition using adaptive and robust correlation filters
Computer Vision and Image Understanding
Face recognition across pose: A review
Pattern Recognition
Pattern Recognition Letters
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In this paper, we have selected some recent advanced correlation filters: minimum average correlation filter (MACE), unconstrained MACE filter (UMACE), phase-only unconstrained MACE filter (POUMACE), distance-classifier correlation filter (DCCF) [B.V.K. Vijaya Kumar, D. Casasent, A. Mahalanobis, Distance-classifier correlation filters for multiclass target recognition. Appl. Opt. 35 (1996) 3127-3133] and minimax distance transform correlation filter (MDTC) and used them to test recognition performance in different situations involving variations in facial expression, illumination conditions and head pose. The paper introduces the first application of correlation filter classifiers to facial images subject to head pose variations. It also demonstrates that it is possible to obtain illumination invariance without using any training images for this purpose. A comparison of MDTC with traditional discriminant learning methods (e.g., KPCA [Scholikopf, B., Smola, A., Muller, K.R., Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput., 10 (1999) 1299-1319], IPCA [16], GDA [Baudat, G., Anouar, F., Generalized discriminant analysis using a kernel approach. Neural Comput., 12 (2000) 2385-2404], R-KDA [Lu, J., Plataniotis, K., Venetsanopoulos, A. Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition. Pattern Recogn. Lett., 26 (2) (2005) 181-191)] is also presented. The paper shows that correlation filter classifiers, a relatively unheralded model-based approach, have a greater robustness and accuracy than traditional appearance-based methods (such as PCA). Overall, the POUMACE filter provided the best choice for facial matching. It achieved 100% accuracy on the publicly available CMU facial expression database and the Yale frontal face illumination database, and slightly less in the head pose experiments.