Face Hallucination: How much it can improve face recognition
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Face hallucination has been a popular topic in image processing in recent years. Currently the commonly used performance criteria for face hallucination are peak signal noise ratio (PSNR) and the root mean square error (RMSE). Though it is logically believed that hallucinated high-resolution face images should have a better performance in face recognition, we show in this paper that this `the higher resolution, the higher recognition' assumption is not validated systematically by some designed experiments. First, we illustrate this assumption only works when the image solution is sufficiently large. Second, in the case of very extreme low resolutions, the recognition performance of the hallucinated images obtained by some typical existing face hallucination approaches will not improve. Finally, the relationship of the popular evaluation methods in face hallucination, PSNR and RMSE, with the recognition performance are investigated. The findings of this paper can help people design new hallucination approaches with an aim of improving face recognition performance with specified classifiers.
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