Hardware/Software Co-design for a Gender Recognition Embedded System

  • Andrew Tzer-Yeu Chen
  • Morteza Biglari-Abhari
  • Kevin I-Kai Wang
  • Abdesselam Bouzerdoum
  • Fok Hing Chi Tivive
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9799)

Abstract

Gender recognition has applications in human-computer interaction, biometric authentication, and targeted marketing. This paper presents an implementation of an algorithm for binary male/female gender recognition from face images based on a shunting inhibitory convolutional neural network, which has a reported accuracy on the FERET database of 97.2 %. The proposed hardware/software co-design approach using an ARM processor and FPGA can be used as an embedded system for a targeted marketing application to allow real-time processing. A threefold speedup is achieved in the presented approach compared to a software implementation on the ARM processor alone.

Keywords

Real-time Embedded system Computer vision FPGA Neural network Co-design Hardware acceleration 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Andrew Tzer-Yeu Chen
    • 1
  • Morteza Biglari-Abhari
    • 1
  • Kevin I-Kai Wang
    • 1
  • Abdesselam Bouzerdoum
    • 2
  • Fok Hing Chi Tivive
    • 2
  1. 1.Department of Electrical and Computer EngineeringUniversity of AucklandAucklandNew Zealand
  2. 2.School of Electrical, Computer and Telecommunications EngineeringUniversity of WollongongWollongongAustralia

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