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

An evolving spatio-temporal approach for gender and age group classification with Spiking Neural... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Evolving Systems Springer Journals

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

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by Springer-Verlag Berlin Heidelberg
Subject
Engineering; Complexity; Artificial Intelligence (incl. Robotics); Complex Systems
ISSN
1868-6478
eISSN
1868-6486
D.O.I.
10.1007/s12530-017-9175-y
Publisher site
See Article on Publisher Site

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.

Journal

Evolving SystemsSpringer Journals

Published: Feb 17, 2017

References

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