Improved Selective Refinement Network for Face Detection

TL;DR

This paper enhances the Selective Refinement Network for face detection by integrating multiple techniques, resulting in state-of-the-art performance on the WIDER FACE benchmark.

Contribution

It introduces an improved SRN by combining data augmentation, backbone upgrades, pretraining, and additional modules, achieving superior face detection accuracy.

Findings

Achieved top performance on WIDER FACE dataset

Improved detection accuracy with combined techniques

Identified effective and ineffective enhancements

Abstract

As a long-standing problem in computer vision, face detection has attracted much attention in recent decades for its practical applications. With the availability of face detection benchmark WIDER FACE dataset, much of the progresses have been made by various algorithms in recent years. Among them, the Selective Refinement Network (SRN) face detector introduces the two-step classification and regression operations selectively into an anchor-based face detector to reduce false positives and improve location accuracy simultaneously. Moreover, it designs a receptive field enhancement block to provide more diverse receptive field. In this report, to further improve the performance of SRN, we exploit some existing techniques via extensive experiments, including new data augmentation strategy, improved backbone network, MS COCO pretraining, decoupled classification module, segmentation branch and Squeeze-and-Excitation block. Some of these techniques bring performance improvements, while few of them do not well adapt to our baseline. As a consequence, we present an improved SRN face detector by combining these useful techniques together and obtain the best performance on widely used face detection benchmark WIDER FACE dataset.