Towards a Deep Learning Framework for Unconstrained Face Detection

TL;DR

This paper introduces MS-FRCNN, a deep learning framework designed to improve unconstrained face detection in challenging real-world conditions, achieving state-of-the-art results on benchmark datasets.

Contribution

The paper presents a novel multi-scale CNN architecture for robust face detection under various challenging conditions, outperforming existing methods.

Findings

Achieves high accuracy on Wider Face and FDDB datasets.

Outperforms recent face detection methods in challenging scenarios.

Demonstrates robustness to occlusions, low resolutions, and illumination variations.

Abstract

Robust face detection is one of the most important pre-processing steps to support facial expression analysis, facial landmarking, face recognition, pose estimation, building of 3D facial models, etc. Although this topic has been intensely studied for decades, it is still challenging due to numerous variants of face images in real-world scenarios. In this paper, we present a novel approach named Multiple Scale Faster Region-based Convolutional Neural Network (MS-FRCNN) to robustly detect human facial regions from images collected under various challenging conditions, e.g. large occlusions, extremely low resolutions, facial expressions, strong illumination variations, etc. The proposed approach is benchmarked on two challenging face detection databases, i.e. the Wider Face database and the Face Detection Dataset and Benchmark (FDDB), and compared against recent other face detection methods, e.g. Two-stage CNN, Multi-scale Cascade CNN, Faceness, Aggregate Chanel Features, HeadHunter, Multi-view Face Detection, Cascade CNN, etc. The experimental results show that our proposed approach consistently achieves highly competitive results with the state-of-the-art performance against other recent face detection methods.