Facial Image Reconstruction by SVDD-Based Pattern De-noising
Jooyoung Park, Daesung Kang, James T. Kwok, Sang-Woong Lee, Bon-Woo Hwang, Seong-Whan Lee
Abstract:
The SVDD (support vector data description) is one of the
most well-known one-class support vector learning methods, in which one
tries the strategy of utilizing balls defined on the feature space in order to
distinguish a set of normal data from all other possible abnormal objects.
In this paper, we consider the problem of reconstructing facial images
from the partially damaged ones, and propose to use the SVDD-based
de-noising for the reconstruction. In the proposed method, we deal with
the shape and texture information separately. We first solve the SVDD
problem for the data belonging to the given prototype facial images, and
model the data region for the normal faces as the ball resulting from the
SVDD problem. Next, for each damaged input facial image, we project
its feature vector onto the decision boundary of the SVDD ball so that
it can be tailored enough to belong to the normal region. Finally, we
obtain the image of the reconstructed face by obtaining the pre-image of
the projection, and then further processing with its shape and texture
information. The applicability of the proposed method is illustrated via
some experiments dealing with damaged facial images.
Proceedings of the International Conference on Biometrics
(ICB'2006),
pp.129-135,
Hong Kong, 2006.
Pdf:
http://www.cs.ust.hk/~jamesk/papers/icb06.pdf
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