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Hardware/software co-design for a gender recognition embedded system

Journal Article


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.

Publication Date


  • 2016

Citation


  • A. Tzer-Yeu. Chen, M. Biglari-Abhari, K. I-Kai. Wang, A. Bouzerdoum & F. Tivive , "Hardware/software co-design for a gender recognition embedded system," Lecture notes in computer science, vol. 9799, pp. 541-552, 2016.

Scopus Eid


  • 2-s2.0-84978906211

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5768

Number Of Pages


  • 11

Start Page


  • 541

End Page


  • 552

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.

Publication Date


  • 2016

Citation


  • A. Tzer-Yeu. Chen, M. Biglari-Abhari, K. I-Kai. Wang, A. Bouzerdoum & F. Tivive , "Hardware/software co-design for a gender recognition embedded system," Lecture notes in computer science, vol. 9799, pp. 541-552, 2016.

Scopus Eid


  • 2-s2.0-84978906211

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5768

Number Of Pages


  • 11

Start Page


  • 541

End Page


  • 552

Volume


  • 9799