FHEDN: A based on context modeling Feature Hierarchy Encoder-Decoder Network for face detection

TL;DR

This paper introduces FHEDN, a context-aware encoder-decoder network designed to improve face detection in challenging outdoor conditions, especially for small, blurred, and occluded faces, achieving strong benchmark results.

Contribution

The paper proposes a novel feature hierarchy encoder-decoder architecture that enhances detection of difficult faces by modeling context at multiple levels.

Findings

Achieves promising results on WIDER FACE and FDDB benchmarks.

Effectively detects small, blurred, and occluded faces.

Analyzes training set distribution, default box scale, and receptive field impact.

Abstract

Because of affected by weather conditions, camera pose and range, etc. Objects are usually small, blur, occluded and diverse pose in the images gathered from outdoor surveillance cameras or access control system. It is challenging and important to detect faces precisely for face recognition system in the field of public security. In this paper, we design a based on context modeling structure named Feature Hierarchy Encoder-Decoder Network for face detection(FHEDN), which can detect small, blur and occluded face with hierarchy by hierarchy from the end to the beginning likes encoder-decoder in a single network. The proposed network is consist of multiple context modeling and prediction modules, which are in order to detect small, blur, occluded and diverse pose faces. In addition, we analyse the influence of distribution of training set, scale of default box and receipt field size to detection performance in implement stage. Demonstrated by experiments, Our network achieves promising performance on WIDER FACE and FDDB benchmarks.