Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
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Computer Vision and Image Understanding - Special issue on Face recognition
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ACM Computing Surveys (CSUR)
Online and batch learning of pseudo-metrics
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
Automatic basis function construction for approximate dynamic programming and reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Component-based face recognition with 3D morphable models
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
2D and 3d multimodal hybrid face recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Face verification using indirect neighbourhood components analysis
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
Double linear regressions for single labeled image per person face recognition
Pattern Recognition
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Subspace methods have been successfully applied to face recognition tasks. In this study we propose a face recognition algorithm based on a linear subspace projection. The subspace is found via utilizing a variant of the neighbourhood component analysis (NCA) algorithm which is a supervised dimensionality reduction method that has been recently introduced. Unlike previously suggested supervised subspace methods, the algorithm explicitly utilizes the classification performance criterion to obtain the optimal linear projection. In addition to its feature extraction capabilities, the algorithm also finds the optimal distance-metric that should be used for face-image retrieval in the transformed space. The proposed face-recognition technique significantly outperforms traditional subspace-based approaches particulary in very low-dimensional representations. The method performance is demonstrated across a range of standard face databases.