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2018 | OriginalPaper | Chapter

Novel Group Variable Selection for Salient Skull Region Selection and Sex Determination

Authors : Olasimbo Ayodeji Arigbabu, Iman Yi Liao, Nurliza Abdullah, Mohamad Helmee Mohamad Noor

Published in: Advances in Brain Inspired Cognitive Systems

Publisher: Springer International Publishing

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Abstract

Sex determination in forensic analysis involves individual examination of different sites of the skull and combination of these sites to understand their impact on the estimation results. Conventionally, forensic experts perform a stepwise combination of several skull region assessment parameters to determine the most important regions with regard to the sex estimation results. This paper introduces a novel group variable selection algorithm: Graph Laplacian Based Group Lasso with split augmented Lagrangian shrinkage algorithm (SALSA) to automatically learn from data by structuring the data into a set of disjointed groups and imposing a number of group sparsity to discover the salient groups which influence the sex determination results. In order to attain this, the skull is partitioned into smaller regions (local regions) using fuzzy c-means (FCM), which are further arranged into clusters as structured groups. Then, we implement the SALSA based group lasso algorithm to impose sparsity on the groups. Our experiments are conducted on 100 skull samples obtained from hospital kuala lumpur (HKL) and the best estimation result obtained is 84.5%.
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Metadata
Title
Novel Group Variable Selection for Salient Skull Region Selection and Sex Determination
Authors
Olasimbo Ayodeji Arigbabu
Iman Yi Liao
Nurliza Abdullah
Mohamad Helmee Mohamad Noor
Copyright Year
2018
DOI
https://doi.org/10.1007/978-3-030-00563-4_24

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