YOLO-FaceV2: A scale and occlusion aware face detector

• Published in: Pattern recognition Vol. 155; p. 110714
• Main Authors: Yu, Ziping, Huang, Hongbo, Chen, Weijun, Su, Yongxin, Liu, Yahui, Wang, Xiuying
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2024
• Subjects: Face detection; Imbalance problem; Occlusion; Scale-aware; YOLO; YOLO; Occlusion; Imbalance problem; Face detection; Scale-aware
• DOI: 10.1016/j.patcog.2024.110714
• ISSN: 0031-3203

In recent years, face detection algorithms based on deep learning have made great progress. Nevertheless, the effective utilization of face detectors for small and occlusion faces remains challenging, primarily stemming from the limitations in pixel information and the presence of missing features. In this paper, we propose a novel real-time face detector, YOLO-FaceV2, built upon the YOLOv5 architecture. Our approach introduces a Receptive Field Enhancement (RFE) module designed to extract multi-scale pixel information and augment the receptive field for accurately detecting small faces. To address issues related to face occlusion, we introduce an attention mechanism termed the Separated and Enhancement Attention Module (SEAM), which effectively focuses on the regions affected by occlusion. Furthermore, we propose a Slide Weight Function (SWF) to mitigate the imbalance between easy and hard samples. The experiments demonstrate that our YOLO-FaceV2 achieves performance exceeding the state-of-the-art on the WiderFace validation dataset. Source code and pre-trained model are available at https://github.com/Krasjet-Yu/YOLO-FaceV2. •Proposed an YOLO-FaceV2 detector to address face detection.•Good performance under face occlusion and varying scales.•Designed a novel weighting function alleviated the problem of imbalanced samples.•Detection results on the WiderFace validation dataset are 98.6%, 97.9% and 91.9%.•Achieved state-of-the-art performance on the easy and medium subset of WiderFace dataset.