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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9799)


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.


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

  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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