Modeling of Low-Resolution Face Imaging

Abstract

The image captured by the surveillance camera at a large distance yields a low-resolution image. Commonly, researchers recognize a face from a distance by improving the quality of the low-resolution image. After the image has been upgraded, it will be compared with the high-resolution face image saved on the database. But in this paper, the inverse is applied. The model is made by downgrading the quality of the high-resolution image in order to make it similar to the low- resolution image. Some methods in digital image formation are used to make the model. Some experiments also conducted to compare the model with images obtained by the real cameras at various distances. In order to optimize the model, some parameters were used to tune some steps of low-resolution modeling, i.e., scaling, kernel size of the filter, gamma, and compression quality. The result shows that the proposed model can improve the recognition performance on SC face.