Title: Gender classification using a PSO-based feature selection and optimised BPNN in forensic anthropology

Authors: Nurul Liyana Hairuddin; Lizawati Mi Yusuf; Mohd Shahizan Othman; Dewi Nasien

Addresses: Department of Computer Science, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor, Malaysia ' Department of Computer Science, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor, Malaysia ' Department of Computer Science, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor, Malaysia ' Department of Electro Technique, Faculty of Technique, Universitas Riau, Riau, Indonesia

Abstract: Gender classification is a crucial task in most forensic cases. In most cases, skeleton remains are employed and there are different parts of human skeleton available for the classification process. Every part of skeleton contains different types of features which benefits toward gender classification. However, some features cannot contribute toward classification as features carry no information on gender. Hence, this article proposed a particle swarm optimisation-based (PSO) feature selection and optimised BPNN model as a gender classification framework. Initially, PSO selects the most significant features that lead to an accurate classification process. In the BPNN process, the parameter tuning based on cross-validation technique is applied where the model is able to find a good combination of learning rate and momentum. This article utilised data from Goldman Osteometric dataset, Clavicle collection, and George Murray Black collection. The result shows that the accuracy of gender classification is improved for every dataset via the proposed framework.

Keywords: gender classification; forensic anthropology; feature selection; particle swarm optimisation; PSO; back propagation neural network; BPNN; parameter tuning.

DOI: 10.1504/IJCAET.2021.117133

International Journal of Computer Aided Engineering and Technology, 2021 Vol.15 No.2/3, pp.232 - 242

Received: 25 Apr 2018
Accepted: 26 Nov 2018
Published online: 19 Aug 2021 *

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