Abstract
As far as the majority of known aging methods are concerned, PCA (Principal Component Analysis) was used as the first step to extract facial features and build model space. In this paper, NMF (Non-negative Factorization) with sparseness constraints is used as an alternative to PCA in the feature extraction step when aging an unseen human face image to the required age. A variety of experiments demonstrate that by adding sparseness constraints to NMF we can get simulated aging faces which share more similarities with real images than those by the method of PCA, especially when we keep the coefficients sparse while leaving the basis vectors unconstrained.
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Ye, YQ., Du, JX., Zhai, CM. (2010). Aging Simulation of Human Faces Based on NMF with Sparseness Constraints. In: Huang, DS., Zhang, X., Reyes García, C.A., Zhang, L. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2010. Lecture Notes in Computer Science(), vol 6216. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14932-0_82
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DOI: https://doi.org/10.1007/978-3-642-14932-0_82
Publisher Name: Springer, Berlin, Heidelberg
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