Digital Forensics for Skulls Classification in Physical Anthropology Collection Management

作     者：Imam Yuadi Myrtati D.Artaria Sakina A.Taufiq Asyhari 

作者机构：Department of Information and Library ScienceAirlangga UniversitySurabaya60286Indonesia Department of AnthropologyAirlangga UniversitySurabaya60286Indonesia Department of Anatomy and HistologyAirlangga UniversitySurabaya60286Indonesia School of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUnited Kingdom 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第68卷第9期

页      面：3979-3995页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The work of I.Yuadi and A.T.Asyhari has been supported in part by Universitas Airlangga through International Collaboration Funding(Mobility Staff Exchange) 

主　　题：Discrete wavelet transform Gabor gray-level co-occurrence matrix human skulls physical anthropology support vector machine 

摘      要：The size,shape,and physical characteristics of the human skull are distinct when considering individual *** physical anthropology,the accurate management of skull collections is crucial for storing and maintaining collections in a cost-effective *** example,labeling skulls inaccurately or attaching printed labels to skulls can affect the authenticity of *** 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 *** underlying facial bone exhibits unique characteristics essential to the face’s physical structure that could be exploited for ***,we developed an automatic recognition method to classify human skulls for consistent identification compared with traditional classification *** 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 *** 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.