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.
CITATION STYLE
Chen, A. T. Y., Biglari-Abhari, M., Wang, K. I. K., Bouzerdoum, A., & Tivive, F. H. C. (2016). Hardware/software co-design for a gender recognition embedded system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9799, pp. 541–552). Springer Verlag. https://doi.org/10.1007/978-3-319-42007-3_47
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