Low Resolution Face Recognition in Surveillance Systems
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In surveillance systems, the captured facial images are often very small and different from the low-resolution images down-sampled from high-resolution facial images. They generally lead to low performance in face recog-nition. In this paper, we study specific scenarios of face recognition with surveillance cameras. Three important factors that influence face recognition performance are investigated: type of cameras, distance between the ob-ject and camera, and the resolution of the captured face images. Each factor is numerically investigated and analyzed in this paper. Based on these observations, a new approach is proposed for face recognition in real sur-veillance environment. For a raw video sequence captured by a surveillance camera, image pre-processing tech-niques are employed to remove the illumination variations for the enhancement of image quality. The face im-ages are further improved through a novel face image super-resolution method. The proposed approach is proven to significantly improve the performance of face recognition as demonstrated by experiments.
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