DAFE-FD: Density Aware Feature Enrichment for Face Detection
This work addresses small face detection for computer vision applications, offering a complementary method to existing anchor-based approaches, though it is incremental in nature.
The paper tackles the problem of detecting small faces by enriching feature maps using a density map estimation module, achieving improved results on datasets like WIDER, FDDB, and Pascal-Faces.
Recent research on face detection, which is focused primarily on improving accuracy of detecting smaller faces, attempt to develop new anchor design strategies to facilitate increased overlap between anchor boxes and ground truth faces of smaller sizes. In this work, we approach the problem of small face detection with the motivation of enriching the feature maps using a density map estimation module. This module, inspired by recent crowd counting/density estimation techniques, performs the task of estimating the per pixel density of people/faces present in the image. Output of this module is employed to accentuate the feature maps from the backbone network using a feature enrichment module before being used for detecting smaller faces. The proposed approach can be used to complement recent anchor-design based novel methods to further improve their results. Experiments conducted on different datasets such as WIDER, FDDB and Pascal-Faces demonstrate the effectiveness of the proposed approach.