Abstract
Though tremendous strides have been made in face detection, face detection remains a challenging problem due to scale variance. In this paper, we propose a smoothed attention network for performing scale-invariant face detection by taking advantage of feature fusion and context enhancement, which is dubbed SANet. To reduce the noise in the fused features at different levels, an Attention-guided Feature Fusion Module (AFFM) is designed. In addition, an exhaustive analysis of the role of attention mechanism on performance is conducted, which considers channel-wise, spatial-wise attentions and their combinations. To enrich the contextual information by using dilated convolution and avoid the gridding artifacts problem produced by dilated convolution, we propose a Smoothed Context Enhancement Module (SCEM). Our method achieves state-ofthe-art results on the UFDD dataset and comparable performance on the WIDER FACE, FDDB, PASCAL Faces, and AFW datasets.
Original language | English (US) |
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Article number | 9091528 |
Pages (from-to) | 235-244 |
Number of pages | 10 |
Journal | IEEE Transactions on Biometrics, Behavior, and Identity Science |
Volume | 2 |
Issue number | 3 |
DOIs | |
State | Published - Jul 2020 |
Keywords
- Context enhancement
- Face detection
- Feature fusion
- Multi-scale
ASJC Scopus subject areas
- Instrumentation
- Computer Vision and Pattern Recognition
- Computer Science Applications
- Artificial Intelligence