Assessing the Effect of Image Quality on SSD and Faster R-CNN Networks for Face Detection

Abstract

Face detection is one of the most challenging and long-studied areas in computer vision. In real-world, images are exposed to the noise and degradation. In this paper, we investigate the robustness of two networks namely SSD and Faster R-CNN in confrontation with salt and pepper noise, Gaussian blur, as well as JPEG compression. Our experiments are conducted on the well-known Wider Face dataset. These experiments show that the Faster R-CNN is more robust against Gaussian blur, while SSD is much more sensitive to the edges. On the other hand, SSD is more robust against reduced-quality JPEG compressed images. The reason should be due to the sensitivity of Faster R-CNN to the texture of the objects. Moreover, our experiments demonstrated that both networks have a relatively similar resistance under salt and pepper noise.