Advancements In Skull Superimposition Techniques for Forensic Identification
Year: 2024
Type: article
Abstract: Exploration of the evolution of skull superimposition techniques in forensic identification, focusing on recent innovations and their practical applications. The main objective is to evaluate how these advancements have enhanced the accuracy and reliability of forensic identifications. By conducting a comprehensive analysis of the literature, key developments such as the integration of 3D imaging technologies, advanced algorithms, and the use of machine learning were identified and examined.Various software tools currently used in skull superimposition were assessed for their effectiveness based on criteria such as user-friendliness, processing speed, and landmark detection accuracy. The findings reveal that while some tools offer precision and ease of use, others still require significant manual input, indicating a need for further refinement and standardization in the field. Additionally, three case studies were analyzed to demonstrate the practical application and validation of these advanced techniques across different forensic scenarios. These cases illustrate the real-world impact of modernized skull superimposition methods, highlighting improvements in identification outcomes and discussing the challenges encountered.This analysis provides a comprehensive overview of the current state of skull superimposition techniques, identifying both their strengths and areas for improvement. The conclusion emphasizes that despite significant progress, ongoing research and development are crucial to addressing existing limitations and further enhancing the reliability and accuracy of forensic identifications using these methods. It underscores the importance of continuous innovation and collaboration within the forensic science community.
Author: C.A, Thayanithi
Institution: Amity University
Language: English
Subfield: Archeology
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6893163301
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