Abstract
In this research, we use Support Vector Machines (SVMs) in our system to automatically synthesize the aging effect on human facial images. The coordinates of the facial feature points are used to train the SVMs. Given a new picture, the displacement of the feature points is predicted according to different target ages in the future. The predictions are fed into a warping system to produce the synthesized aged facial images. The results of the prediction using SVMs are analyzed.
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© 2004 Springer-Verlag Berlin Heidelberg
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Wang, J., Ling, C.X. (2004). Artificial Aging of Faces by Support Vector Machines. In: Tawfik, A.Y., Goodwin, S.D. (eds) Advances in Artificial Intelligence. Canadian AI 2004. Lecture Notes in Computer Science(), vol 3060. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24840-8_44
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DOI: https://doi.org/10.1007/978-3-540-24840-8_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22004-6
Online ISBN: 978-3-540-24840-8
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