Abstract
Face hallucination refers to inferring an High-Resolution (HR) face image from the input Low-Resolution (LR) one. It plays a vital role in LR face recognition by both manual and computer. The eigentransformation method based on Principal Component Analysis (PCA), which represents face image as a linear combination of the eigenfaces, has attracted considerable interests because of its simplicity and effectiveness. However, the face image observed is in a high-dimensional non-linear space, whose statistical properties cannot be captured by the PCA based linear modeling method. To this end, in this paper we advance a Data-driven Local Eigentransformation (DLE) method for face hallucination by exploiting the local geometry structure of data manifold and learning a specified eigentransformation model for each observed image. Experimental results show the effectiveness of the proposed approach for hallucinating face images especially with noise.
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Dong, X., Hu, R., Jiang, J., Han, Z., Chen, L., Gao, G. (2014). Noise Face Image Hallucination via Data-Driven Local Eigentransformation. In: Ooi, W.T., Snoek, C.G.M., Tan, H.K., Ho, CK., Huet, B., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2014. PCM 2014. Lecture Notes in Computer Science, vol 8879. Springer, Cham. https://doi.org/10.1007/978-3-319-13168-9_19
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DOI: https://doi.org/10.1007/978-3-319-13168-9_19
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