Abstract
This paper presents a new approach to generate a high quality facial image from a low resolution facial image, based on a large set of facial images belongs to the same person but varies in pose and expression. The input images are taken by low-end cameras or cameras from a long distance. The facial poses and expressions are not consistent and aligned. Firstly, using a low resolution facial image as a query image, a set of high resolution images with similar pose and expression is retrieved from the image examples by the proposed similarity measurement based on its shape and texture information of the query image. The selected images are then aligned with the query image and used as the candidates for the face hallucination. A Markov random field (MRF) model based on a new proposed color and edge constraints is introduced to find an optimum solution for the hallucination image. In the experiments, high textural details of hallucination images which are four to eight times larger than the original low resolution images were generated by the proposed face hallucination approach. The high resolution outputs of our method are significantly improved in quality compared to other image superresolution methods. Moreover, we also showed that our new approach is able to handle underexposure and noisy images.
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Loke, Y.R., Tan, P., Kassim, A.A. (2013). Face Hallucination on Personal Photo Albums. In: Park, JI., Kim, J. (eds) Computer Vision - ACCV 2012 Workshops. ACCV 2012. Lecture Notes in Computer Science, vol 7729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37484-5_24
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DOI: https://doi.org/10.1007/978-3-642-37484-5_24
Publisher Name: Springer, Berlin, Heidelberg
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