A Surveillance System Using CNN for Face Recognition with Object, Human and Face Detection

  • Yeong-Hyeon Byeon
  • Sung-Bum Pan
  • Sang-Man Moh
  • Keun-Chang KwakEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 376)


Recently, surveillance system plays an important role in solving several crimes by replacing human to watch monitors. Not only many functions are complicatedly integrated to a system but also a system is evolved to capture statistical data to extract useful information. But integrating many functions should be considered to make it have reduced processing time because a system has limited processing ability. If a system considers moving objects, it could reduce processing time because surveillance system normally has static background that is useless information. People and face detection are performed in detected objects. Detected faces are recognized using CNN(Convolutional Neural Network). The processing time of the proposed system is reduced and true rate of face recognition is 72.7% under varying distance from 2m to 5m.


Surveillance CNN Face recognition Object detection 


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Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Yeong-Hyeon Byeon
    • 1
  • Sung-Bum Pan
    • 1
  • Sang-Man Moh
    • 2
  • Keun-Chang Kwak
    • 1
    Email author
  1. 1.Department of Control and Instrumentation EngineeringChosun UniversityGwangjuKorea
  2. 2.Department of Computer EngineeringChosun UniversityGwangjuKorea

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