This paper presents an approach to postmortem interval (PMI) estimation, which is a very debated and complicated area of forensic science. Most of the reported methods to determine PMI in the literature are not practical because of the need for skilled persons and significant amounts of time, and give unsatisfactory results. Additionally, the error margin of PMI estimation increases proportionally with elapsed time after death. It is crucial to develop practical PMI estimation methods for forensic science. In this study, a computational system is developed to determine the PMI of human subjects by investigating postmortem opacity development of the eye. Relevant features from the eye images were extracted using image processing techniques to reflect gradual opacity development. The features were then investigated to predict the time after death using machine learning methods. The experimental results prove that the development of opacity can be utilized as a practical computational tool to determine PMI for human subjects.
Keywords: Forensic medicine; Forensic science; Image processing; Postmortem interval estimation; Time after death; Time of death.
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