Abstract
Fisherface is a popular subspace algorithm used in face recognition, and is commonly believed superior to another technique, Eigenface, due to its attempt to maximize the separability of training classes. However, the obtained discriminating subspace of the training set may not easily extend to unseen classes (thus poor generalization), as in the case of enrollment of new subjects. In this paper, we reduce the performance variance and improve the generalization of Fisherface by automatically selecting some representative classes for training, using a recently proposed neural network architecture SOM2. The experiments on ORL face database validate the proposed method.
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Jiang, J., Zhang, L., Furukawa, T. (2006). Improving the Generalization of Fisherface by Training Class Selection Using SOM2 . In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_31
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DOI: https://doi.org/10.1007/11893257_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
Online ISBN: 978-3-540-46482-2
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