Original Contribution: Stacked generalization
Neural Networks
Machine Learning
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Recent advances in visual and infrared face recognition: a review
Computer Vision and Image Understanding
Generalized unified decomposition of ensemble loss
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Face recognition using LDA-based algorithms
IEEE Transactions on Neural Networks
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In this paper, we propose a hierarchical method for face recognition where base classifiers are defined to make predictions based on various different principles and classifications are combined into a single prediction. Some features are more relevant to particular face recognition tasks than others. The hierarchical algorithm is flexible in selecting features relevant for the face recognition task at hand. In this paper, we explore various features based on outline recognition, PCA classifiers applied to part of the face and exploitation of symmetry in faces. By combining the predictions of these features we obtain superior performance on benchmark datasets (99.25% accuracy on the ATT dataset) at reduced computation cost compared to full PCA.