A systematic review of CNN-based model approaches for human identification using bitemark evidence
Sonali A. Mohite
, Bhavna Narain , B.T.Jadhav
F1-score, three-dimensional cast, label encoding, probability distribution, convolutional layers, pooling layers, fully connected layers.
A total of 22 recent three-year papers were examined in this paper to examine the application of CNN models to digital images. In forensic dentistry, the use of bitemark evidence for human identity is nothing new. Convolutional Neural Networks (CNNs) have been used for the examination of bitemark evidence thanks to recent developments in machine learning. An abstract of a project whose objective is to create a CNN model for automatically identifying human bitemarks based on dental traits is presented in this publication. The suggested model optimises on a dataset of dental pictures using a pre-trained CNN architecture. A variety of bitemarks with various patterns, shapes, and sizes make up the dataset. The model is trained to recognise particular dental characteristics like tooth size, orientation, and spacing to precisely match bitemarks to the offender. Accuracy, precision, recall, and F1-score measures are used to assess the performance of the suggested model. The findings demonstrate that the suggested CNN model can distinguish human bitemarks with good precision and recall rates. The suggested model may enhance forensic dentistry's precision and effectiveness when identifying people based on their bitemarks.
" A systematic review of CNN-based model approaches for human identification using bitemark evidence", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.10, Issue 7, page no.b835-b842, July-2025, Available :https://ijsdr.org/papers/IJSDR2507197.pdf
Volume 10
Issue 7,
July-2025
Pages : b835-b842
Paper Reg. ID: IJSDR_304294
Published Paper Id: IJSDR2507197
Downloads: 000492
Research Area: Science and Technology
Country: Satara, Maharashtra, India
DOI: https://doi.org/10.56975/ijsdr.v10i7.304294
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
