Self-organizing maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition with one training image per person
Pattern Recognition Letters
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Feature Selection for Face Recognition Based on Data Partitioning
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Enhanced (PC)2 A for face recognition with one training image per person
Pattern Recognition Letters
Journal of Cognitive Neuroscience
IEEE Transactions on Neural Networks
Face recognition from a single image per person: A survey
Pattern Recognition
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While feature selection is very difficult for high dimensional, unstructured data such as face image, it may be much easier to do if the data can be faithfully transformed into lower dimensional space. In this paper, a new method is proposed to transform the high dimensional face images into low-dimensional SOM topological space, and then identify important local features of face images for face recognition automatically using simple statistics computed from the class distribution of the face image data. The effectiveness of the proposed method are demonstrated by the experiments on AR face databases, which reveal that up to 80% local features can be pruned with only slightly loss of the classification accuracy.