Inverted Non-maximum Suppression for more Accurate and Neater Face Detection
This addresses a specific issue in face detection for applications like surveillance or social media, but it is incremental as it modifies an existing post-processing step.
The paper tackles the problem of inaccurate face detection in low-quality or clustered images by proposing an inverted non-maximum suppression method, which reduces multiple predicted boxes per face and shows effectiveness as a post-processor across different detection methods.
CNN-based face detection methods have achieved significant progress in recent years. In addition to the strong representation ability of CNN, post-processing methods are also very important for the performance of face detection. In general, the face detection method predicts several candidate bounding-boxes for one face. NMS is used to filter out inaccurate candidate boxes to get the most accurate box. The principle of NMS is to select the box with a higher score as the basic box and then delete the box which has a large overlapping area with the basic box but has a lower score. However, the current NMS method and its improved versions do not perform well when face image quality is poor or faces are in a cluster. In these situations, even after NMS filtering, there is often a face corresponding to multiple predicted boxes. To reduce this kind of negative result, in this paper, we propose a new NMS method that operates in the reverse order of other NMS methods. Our method performs well on low-quality and tiny face samples. Experiments demonstrate that our method is effective as a post-processor for different face detection methods.