Comparison of super-resolution benefits for downsampled iages and real low-resolution data

Recently, more and more researchers are exploring the benefits of super-resolution methods on low-resolution face recognition. However, often results presented are obtained on downsampled high-resolution face images. Because downsampled images are different from real images taken at low resolution, it is important to include real surveillance data. In this paper, we investigate the difference between downsampled images and real surveillance data in two aspects: (1) the influence of resolution on face recognition accuracy, and (2) the improvement of accuracy that can be achieved by super-resolution on these images. Specifically, we will test the following hypotheses: (1) face recognition performance on real images is much worse than on downsampled images, and (2) super-resolution improves the performance of downsampled images more than real images. Our experiments are conducted using videos from the HumanID database. In each video, the target person’s face is captured while he is walking towards the surveillance camera. We detect the faces in the video frames using a Viola-Jones face detector. Then we select face images of four different resolutions: two low-resolution and two high-resolution. The high-resolution images are used for gallery and generating downsampled images. We perform two types of face recognition experiments. In the first type of experiments, three face recognition methods are evaluated for images with different resolution. The three methods are (1) Principal Component Analysis, (2) Linear Discriminant Analysis, and (3) Local Binary Patterns. In the second type, we apply two super-resolution methods: (1) a model based method and (2) a feature based method on the low-resolution (both real and downsampled) images and then compute the face recognition accuracy.