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

Fahad Alvi, Russel Pears, Nikola Kasabov

Research output: Contribution to journalArticle

2 Citations (Scopus)


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 preprocessed 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 neighbour (Knn) and Multi-Layer Perceptron (MLP) with NeuCube. Our empirical results show that NeuCube performed consistently better across both problem types that we investigated.
Original languageEnglish
Pages (from-to)145-156
Number of pages11
JournalEvolving Systems
Issue number(2018)
Early online date17 Feb 2017
Publication statusPublished - 30 Jun 2018



  • Anthropometric model
  • Age grpoup classification
  • Geneder classification
  • Spiking neural networks

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