Abstract:

Abstract- Facial reconstruction from skeletal remains plays a crucial role in forensic anthropology, aiding in the identification of unknown individuals. Central to this process is the accurate estimation of facial soft tissue thickness (FSTT) over a human skull. This paper explores a computational approach using Python programming for developing predictive models of FSTT and automating the skull-face overlay process.The methodology begins with comprehensive data collection involving anthropometric measurements and imaging data from diverse populations. High- resolution CT scans or laser scans of skulls are used to generate accurate 3D models. Anthropometric measurements including age, sex, ancestry, and other relevant factors are meticulously recorded.Statistical modelling techniques, implemented through Python libraries such as NumPy and Pandas, are employed to analyze the collected data. Regression analysis is utilized to establish correlations between skull morphology, demographic variables, and FSTT. Machine learning algorithms from Scikit-learn are integrated to refine predictive models, leveraging the computational efficiency and flexibility of Python. Practical applications of the developed models include forensic anthropology, where accurate facial reconstructions assist in the identification of deceased individuals. Additionally, the models find utility in medical fields for preoperative planning in craniofacial surgery and other interventions requiring precise anatomical knowledge.This paper contributes to the interdisciplinary field of forensic anthropology and medical imaging by presenting a robust computational framework for modelling FSTT using Python, underscoring its practical implications in both forensic and medical sciences.

Description:

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