Digital Forensics for Skulls Classification in Physical Anthropology Collection Management

Imam Yuadi,A Taufiq Asyhari,Myrtati D Artaria,Sakina Sakina

doi:10.32604/cmc.2021.015417

Imam Yuadi, A Taufiq Asyhari + Show 2 more

Open Access

https://doi.org/10.32604/cmc.2021.015417

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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.

Highlights

1.1 Background and MotivationResearchers in digital forensics commonly deal with a series of activities, including collecting, examining, identifying, and analyzing the digital artefacts required for obtaining evidence regarding physical object authenticity [1]
We developed an automatic computerized digital forensics approach for human skull identification using feature extraction in tandem with an support vector machine (SVM)
We tested four different feature extraction filters for feature extraction that resulted in different classification accuracies

Summary

Introduction

1.1 Background and MotivationResearchers in digital forensics commonly deal with a series of activities, including collecting, examining, identifying, and analyzing the digital artefacts required for obtaining evidence regarding physical object authenticity [1]. A skull cataloging and retrieval system is a major component of skull collection management Within this system, skulls with lost labels can be identified via an investigation process. Skulls with lost labels can be identified via an investigation process This process includes labeling the collection in the form of a call number attached to each skull. This ensures that the skulls belong to a specific collection and facilitates their identification. This is important for proper documentation, development, maintenance, and enhancement of existing collections and making them available to curators who want to use them according to classification standards [2]

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