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Evolving Systems

, Volume 9, Issue 2, pp 145–156 | Cite as

An evolving spatio-temporal approach for gender and age group classification with Spiking Neural Networks

  • Fahad Bashir Alvi
  • Russel Pears
  • Nikola Kasabov
Original Paper

Abstract

This research study proposes a novel method of inter-related problems in face recognition using the NeuCube neuromorphic computational platform. We investigated age classification and gender recognition. The well-known FG-NET and MORPH Album 2 image gallery were used and anthropometric features were extracted from landmark points on the face. The landmarks were pre-processed with the procrustes algorithm before feature extraction was performed. The Weka machine learning workbench was used to compare the performance of traditional techniques such as the K nearest neighbor (Knn) and Multi-LayerPerceptron (MLP) with NeuCube. Our empirical results show that NeuCube performed consistently better across both problem types that we investigated.

Keywords

Anthropometric model Age group classification Gender classification Spiking neural networks 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Fahad Bashir Alvi
    • 1
  • Russel Pears
    • 2
  • Nikola Kasabov
    • 1
  1. 1.Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand
  2. 2.School of Computing and Mathematical SciencesAuckland University of TechnologyAucklandNew Zealand

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