# DAFE-FD: Density Aware Feature Enrichment for Face Detection

## TL;DR

This paper introduces a density-aware feature enrichment method for face detection that improves small face detection by integrating a density map estimation module to enhance feature maps, complementing anchor-based strategies.

## Contribution

It proposes a novel density map estimation module to enrich feature maps, improving small face detection performance in conjunction with existing anchor-based methods.

## Key findings

- Improved detection accuracy on WIDER, FDDB, and Pascal-Faces datasets.
- Effective enhancement of small face detection performance.
- Complementary to existing anchor design strategies.

## Abstract

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.