How to Choose Deep Face Models for Surveillance System?

  • Vy Nguyen
  • Tien Do
  • Vinh-Tiep Nguyen
  • Thanh Duc Ngo
  • Duc Anh Duong
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 769)

Abstract

Face recognition suits well in situations that subjects may not cooperate, such as surveillance system, which can be deployed to track movements of a newly detected thief. In this retrieval task, the choice of face representation is highly important. The rise of Deep Learning in Computer Vision has led to the rise of deep models in face recognition, such as FaceNet, DeepFace, VGG Face B, CenterLoss C, VIPLFaceNet, ...  However, when it comes to applications, which model should be chosen to ensure the balance amongst accuracy, computational cost and memory resource is still an open problem. In this work, evaluations some of state-of-the-art deep models (VGG Face B, CenterLoss C, VIPLFaceNet) were conducted under different settings and benchmark protocols to illustrate the trade-offs and draw conclusions not clearly indicated in the original works.

Keywords

Face recognition Deep learning Evaluation Identification Face feature extraction Face representation 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Vy Nguyen
    • 1
  • Tien Do
    • 1
  • Vinh-Tiep Nguyen
    • 1
  • Thanh Duc Ngo
    • 1
  • Duc Anh Duong
    • 1
  1. 1.Multimedia Communications LaboratoryUniversity of Information Technology, Vietnam National UniversityHo Chi Minh CityVietnam

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