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

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.

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Correspondence to Fahad Bashir Alvi.

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Alvi, F.B., Pears, R. & Kasabov, N. An evolving spatio-temporal approach for gender and age group classification with Spiking Neural Networks. Evolving Systems 9, 145–156 (2018). https://doi.org/10.1007/s12530-017-9175-y

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Keywords

  • Anthropometric model
  • Age group classification
  • Gender classification
  • Spiking neural networks