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 AlviEmail author
  • Russel Pears
  • Nikola Kasabov
Original Paper


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.


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
    Email author
  • 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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