摘要
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 recognition. 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 object 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 surveillance environment. For a raw video sequence captured by a surveillance camera, image pre-processing techniques are employed to remove the illumination variations for the enhancement of image quality. The face images 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.
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 recognition. 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 object 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 surveillance environment. For a raw video sequence captured by a surveillance camera, image pre-processing techniques are employed to remove the illumination variations for the enhancement of image quality. The face images 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.
