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- W2349179457 abstract "Most subspace methods for the face recognition, such as linear discriminant analysis, discriminant eigenfeatures and fisherface cannot work in the situation of only one training image available per person, while the eigenface technique suffers a great performance drop. A novel method is used to automatically select some important local areas of a face image for face recogn ition with single training image per person. The SOM output space is studied fro m a novel view, where some enlightening ideas from the automatic text analysis f ield are introduced into the face recognition to evaluate the weights of differe nt local areas of face images. The extracted weight information is then used to select some important local facial features for the recognition. Experiments on both the FERET and ORL face database show that the proposed method is more effic ient than its non-weighted counterpart while keeping the recognition accuracy a cceptable. Moreover, the experiment points out that the most important local are a to recognition maybe not those common supposed local areas, such as ear, nose or mouse." @default.
- W2349179457 created "2016-06-24" @default.
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- W2349179457 date "2005-01-01" @default.
- W2349179457 modified "2023-09-25" @default.
- W2349179457 title "Finding Important Sub-Areas for Face Recognition from Single Training Image Per Person" @default.
- W2349179457 hasPublicationYear "2005" @default.
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