Abstract
Scale variation stands out as one of key challenges in face detection. Recent attempts have been made to cope with this issue by incorporating image/feature pyramids or adjusting anchor sampling/matching strategies. In this work, we propose a novel scale-aware progressive training mechanism to address large scale variations across faces. Inspired by curriculum learning, our method gradually learns large-to-small face instances. The preceding models learned with easier samples (i.e., large faces) can provide good initialization for succeeding learning with harder samples (i.e., small faces), ultimately deriving a better optimum of face detectors. Moreover, we propose an auxiliary anchor-free enhancement module to facilitate the learning of small faces by supplying positive anchors that may be not covered according to the criterion of IoU overlap. Such anchor-free module will be removed during inference and hence no extra computation cost is introduced. Extensive experimental results demonstrate the superiority of our method compared to the state-of-the-arts on the standard FDDB and WIDER FACE benchmarks. Especially, our ProgressFace-Light with MobileNet-0.25 backbone achieves 87.9% AP on the hard set of WIDER FACE, surpassing largely RetinaFace with the same backbone by 9.7%. Code and our trained face detection models are available at https://github.com/jiashu-zhu/ProgressFace.
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Notes
- 1.
Faces with area <128 accounts for \(\sim \)29% in WIDER FACE.
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Zhu, J., Li, D., Han, T., Tian, L., Shan, Y. (2020). ProgressFace: Scale-Aware Progressive Learning for Face Detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12351. Springer, Cham. https://doi.org/10.1007/978-3-030-58539-6_21
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