Smart Media Journal (스마트미디어저널)

THE KOREAN INSTITUTE OF SMART MEDIA (한국스마트미디어학회)
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Tracking by Detection of Multiple Faces using SSD and CNN Features

  • Tai, Do Nhu (Department of Electronics and Computer Engineering, Chonnam National University) ;
  • Kim, Soo-Hyung (Department of Electronics and Computer Engineering, Chonnam National University) ;
  • Lee, Guee-Sang (Department of Electronics and Computer Engineering, Chonnam National University) ;
  • Yang, Hyung-Jeong (Department of Electronics and Computer Engineering, Chonnam National University) ;
  • Na, In-Seop (Software Convergence Education Institute, Chosun University) ;
  • Oh, A-Ran (Department of Electronics and Computer Engineering, Chonnam National University)
  • Received : 2018.09.14
  • Accepted : 2018.10.12
  • Published : 2018.12.31
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Abstract

Multi-tracking of general objects and specific faces is an important topic in the field of computer vision applicable to many branches of industry such as biometrics, security, etc. The rapid development of deep neural networks has resulted in a dramatic improvement in face recognition and object detection problems, which helps improve the multiple-face tracking techniques exploiting the tracking-by-detection method. Our proposed method uses face detection trained with a head dataset to resolve the face deformation problem in the tracking process. Further, we use robust face features extracted from the deep face recognition network to match the tracklets with tracking faces using Hungarian matching method. We achieved promising results regarding the usage of deep face features and head detection in a face tracking benchmark.

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References

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