Multi-task Cascaded Convolutional Neural Networks for Real-Time Dynamic Face Recognition Method

  • Bin Jiang
  • Qiang Ren
  • Fei Dai
  • Jian Xiong
  • Jie Yang
  • Guan GuiEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 517)


Due to the variety of poses, lighting, and scenes, dynamic face detection and calibration pose a big challenge under unconstrained environment. In this paper, we use the inherent correlation between detection and calibration to enhance their performance in a deep multi-task cascaded convolutional neural network (MTCNN). In addition, we utilize Google’s FaceNet framework to learn a mapping from face images to a compact Euclidean space, where distances directly correspond to a measure of face similarity to extract the performance of facial feature algorithms. In the practical application scenario, we set up a multi-camera real-time monitoring system to perform face matching and recognition of collected continuous frames from different angles in real time.


Multi-task cascaded convolutional neural networks Real-time dynamic face recognition FaceNet Facial feature algorithms 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Bin Jiang
    • 1
  • Qiang Ren
    • 1
  • Fei Dai
    • 1
  • Jian Xiong
    • 1
  • Jie Yang
    • 1
  • Guan Gui
    • 1
    Email author
  1. 1.College of Telecommunication and Information Engineering, Nanjing University of Posts and TelecommunicationsNanjingChina

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