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In this paper, we propose a facial skin beautification framework to remove facial spots based on layer dictionary learning and sparse representation. More precisely, we first decompose the face image into three layers: lighting layer, detail layer and color layer. The corresponding detail layer dictionary are learned by using 60 thousands beauty images collected from the Internet. Thereafter, the...
In this paper, we propose a novel illumination-normalization method. By using the combination of the Kernel Principal Component Analysis (KPCA) and Pre-image technology, this method can restore the frontal-illuminated face image from a single non-frontal-illuminated face image. In this method, a frontal-illumination subspace is first learned by KPCA. For each input face image, we project its large-scale...
This paper demonstrates how kernel principal component analysis (KPCA) can be used for face hallucination. Different with other KPCA-based methods, KPCA in this paper handles samples from two subspaces, namely the high- and low-resolution image spaces. As KPCA learns not only linear features but also non-linear features, it is anticipated that more detailed facial features could be synthesized. We...
It is well known that the effect of illumination is mainly on the large-scale features (low-frequency components) of a face image. In solving the illumination problem for face recognition, most (if not all) existing methods either only use extracted small-scale features while discard large-scale features, or perform normalization on the whole image. In the latter case, small-scale features may be...
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