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YOLO-FD: YOLO for Face Detection

  • Luan P. e SilvaEmail author
  • Júlio C. Batista
  • Olga R. P. Bellon
  • Luciano Silva
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11896)

Abstract

Face detection is a fundamental step for any face analysis approach. However, it remains as an unsolved problem in computer vision, specially, when it comes to the variability and distractions of in-the-wild environments. Moreover, a face detector must be accurate and fast to be used in surveillance/biometrics scenarios. In order to overcome these limitations, this paper proposes a customized version of the state-of-the-art object detector, YOLOv3, for face detection. The modifications aim at building a real-time, accurate model capable of detecting faces as small as 16 pixels in 34 FPS. Furthermore, this model was evaluated on three of the most difficult benchmarks for face detection, Wider Faces, UCCS and UFDD, showing a good score balance across them. Also, the comparison with the state-of-the-art shown that it was possible to achieve the second best FPS and the fifth best score on Wider Faces. Finally, the model will be available in https://github.com/luanps/yolofd.

Notes

Acknowledgments

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001 The authors gratefully acknowledge the contribution of reviewers’ comments, etc. (if desired). Put sponsor acknowledgments in the unnumbered footnote on the first page.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.IMAGO-UFPR Research Group, Departmento de InformáticaUniversidade Federal do ParanáCuritibaBrazil

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