کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
410745 | 679162 | 2008 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A class density approximation neural network for improving the generalization of Fisherface
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Fisherface is a popular dimensionality reduction technique in face recognition. The obtained discriminative subspace, however, may not generalize well to unseen classes, as in the case of enrollment of new identities. In this paper, a class density approximation network based on SOM2 is introduced to derive “prototype classes” for training, which improves the generalization of Fisherface and reduces its performance variance resulting from random selections of training classes. We also propose a splitting method to estimate the number of prototype classes needed in approximation. Experiments on synthesized data and real face databases validate the effectiveness of our method.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 71, Issues 16–18, October 2008, Pages 3239–3246
Journal: Neurocomputing - Volume 71, Issues 16–18, October 2008, Pages 3239–3246
نویسندگان
Jiayan Jiang, Liming Zhang, Tetsuo Furukawa,