Revolutionizing Skull Classification: Leveraging Digital Forensics and Deep Learning in Physical Anthropology

The human skull's dimensions, shape, and physical characteristics differ from person to person. Skull collections need to be handled carefully if physical anthropology collections are to be preserved and kept affordably. For example, the authenticity of collections may be jeopardized if skulls are labelled with printed material or given erroneous names. As manual skull recognition is a tedious process, we propose a deep learning (DL) approach and various feature extraction techniques(Fractal features) and feature combinations, to classify human skulls automatically. Every existing facial bone has unique properties that are important to the skull's physical composition and can be utilized to identify an individual. Consequently, we created a Convolutional Neural Network (CNN)-based system for automatically classifying human skulls that reliably distinguishes them more accurately than conventional classification methods. Our proposed technique achieved 99.98% classification accuracy with a loss of 0.01% for skull classification. Our study contributes to the development of an autonomous system by improving the collection of skull pictures through image augmentation using pre-processing approaches for classification. Archaeologists, anthropologists, and forensic scientists may manage, analyze, and preserve anthropological collections more effectively with the help of this creative framework. It can be used in archaeological research, forensic investigations, museum collections, and medical studies. The results highlight the revolutionary possibilities of incorporating digital forensics and machine learning into anthropological research, providing a powerful instrument for expanding our knowledge of human evolution and cultural legacy.