Determination of Age and Sex From Human Bite Mark Using Artificial Intelligence
Introduction:
Bite mark analysis plays a major role in forensic science and crime investigation. It helps in the process of identifying individuals who are involved in criminal activities. It is commonly encountered in crime scenes such as rape, murder, child abuse etc. The process of analysis and the comparison of the bite mark, which is produced by the suspect’s dentition, which is left on human skin, is not an easy task, it is a difficult procedure.
Objectives:
With the help of Artificial Intelligence, the analysis and comparison of unknown bite marks will become more accurate and reliable. The purpose of this study is to create and validate a Convolutional Neural Network (CNN) model for determining the age and sex of individuals from bite mark samples.
Methods:
A dataset comprising 50 bite mark images (25 males and 25 females) from individuals belonging to the 18-25 age category was collected and analyzed using the CNN’s model.
Results and Discussion:
The result shows that the CNN’s model has high accuracy and strong generalization capabilities in the process of classification of bite marks on the basis of sex and age, and the accuracy in sex determination is 97% and in the case of age determination in male it is again 97% and age determination in case of female it is 98%. The performance of this model of architecture indicates that it can serve as a potential tool for the process of investigation and make the identification of the individuals who were involved in the crime more easily. The precision and accuracy of this method is very high due that it can assist in the process more effectively and efficiently. By determining the age and sex of an unknown bite mark, the list of the suspected individuals can be narrowed down and it is very helpful for the investigation process.
Conclusion:
These findings indicate that the CNN model can be used as a valuable technique in forensic investigations, offering a novel, AI-based approach to improve the accuracy and precision of bite mark analysis for age and sex determination. This study also paves the path for additional study and development in AI-driven forensic science.
