Open Access

ARTICLE

Digital Forensics for Skulls Classification in Physical Anthropology Collection Management

Imam Yuadi^1,*, Myrtati D. Artaria², Sakina³, A. Taufiq Asyhari⁴

1 Department of Information and Library Science, Airlangga University, Surabaya, 60286, Indonesia
2 Department of Anthropology, Airlangga University, Surabaya, 60286, Indonesia
3 Department of Anatomy and Histology, Airlangga University, Surabaya, 60286, Indonesia
4 School of Computing and Digital Technology, Birmingham City University, Birmingham, B4 7XG, United Kingdom

* Corresponding Author: Imam Yuadi. Email:

Computers, Materials & Continua 2021, 68(3), 3979-3995. https://doi.org/10.32604/cmc.2021.015417

Received 19 November 2020; Accepted 11 March 2021; Issue published 06 May 2021

Abstract

The size, shape, and physical characteristics of the human skull are distinct when considering individual humans. In physical anthropology, the accurate management of skull collections is crucial for storing and maintaining collections in a cost-effective manner. For example, labeling skulls inaccurately or attaching printed labels to skulls can affect the authenticity of collections. Given the multiple issues associated with the manual identification of skulls, we propose an automatic human skull classification approach that uses a support vector machine and different feature extraction methods such as gray-level co-occurrence matrix features, Gabor features, fractal features, discrete wavelet transforms, and combinations of features. Each underlying facial bone exhibits unique characteristics essential to the face's physical structure that could be exploited for identification. Therefore, we developed an automatic recognition method to classify human skulls for consistent identification compared with traditional classification approaches. Using our proposed approach, we were able to achieve an accuracy of 92.3–99.5% in the classification of human skulls with mandibles and an accuracy of 91.4–99.9% in the classification of human skills without mandibles. Our study represents a step forward in the construction of an effective automatic human skull identification system with a classification process that achieves satisfactory performance for a limited dataset of skull images.

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Keywords

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