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PyramidBox: A Context-Assisted Single Shot Face Detector

  • Xu Tang
  • Daniel K. Du
  • Zeqiang He
  • Jingtuo Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11213)

Abstract

Face detection has been well studied for many years and one of remaining challenges is to detect small, blurred and partially occluded faces in uncontrolled environment. This paper proposes a novel context-assisted single shot face detector, named PyramidBox to handle the hard face detection problem. Observing the importance of the context, we improve the utilization of contextual information in the following three aspects. First, we design a novel context anchor to supervise high-level contextual feature learning by a semi-supervised method, which we call it PyramidAnchors. Second, we propose the Low-level Feature Pyramid Network to combine adequate high-level context semantic feature and Low-level facial feature together, which also allows the PyramidBox to predict faces of all scales in a single shot. Third, we introduce a context-sensitive structure to increase the capacity of prediction network to improve the final accuracy of output. In addition, we use the method of Data-anchor-sampling to augment the training samples across different scales, which increases the diversity of training data for smaller faces. By exploiting the value of context, PyramidBox achieves superior performance among the state-of-the-art over the two common face detection benchmarks, FDDB and WIDER FACE. Our code is available in PaddlePaddle: https://github.com/PaddlePaddle/models/tree/develop/fluid/face_detection.

Keywords

Face detection Context Single shot PyramidBox 

Notes

Acknowledgments

We wish to thank Dr. Shifeng Zhang and Dr. Yuguang Liu for many helpful discussions.

Supplementary material

474192_1_En_49_MOESM1_ESM.pdf (13.1 mb)
Supplementary material 1 (pdf 13372 KB)

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