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2000
Volume 4, Issue 1
  • ISSN: 2950-3779
  • E-ISSN: 2950-3787

Abstract

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 . 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.

© 2025 Bentham Science Publishers
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2025-01-01
2025-09-02

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References

  1. van der VeldenA. SpiessensM. WillemsG. Bite mark analysis and comparison using image perception technology.J. Forensic Odontostomatol.2006241141716783951
     [Google Scholar]
  2. KaurS. KrishanK. ChatterjeeP.M. KanchanT. Analysis and identification of bite marks in forensic casework.Oral Health Dent. Manag.201312312713110.4172/2247‑2452.100050024352302
     [Google Scholar]
  3. MohiteS. NarainB. Bitemark Evidence in Forensic dentistry for Human Identification using deep learning technique.Proceedings of The International Conference on Emerging Trends in Artificial Intelligence and Smart SystemsJabalpur, India, April 16-1710.4108/eai.16‑4‑2022.2318166
     [Google Scholar]
  4. VeytaF. YuniastutiM. SuhartonoA.W. AuerkariE.I. Human bite mark analysis: Review of advantage computer-assisted procedure.AIP Conf. Proc.20222537August03000110.1063/5.0097979
     [Google Scholar]
  5. PatilS. RaoR.S. RajT. A comparison between manual and computerized bite-mark analysis.J. Adv. Oral Res.2013431510.1177/2229411220130301
     [Google Scholar]
  6. RawsonR.D. OmmenR.K. KinardG. JohnsonJ. YfantisA. Statistical evidence for the individuality of the human dentition.J. Forensic Sci.198429124525310.1520/JFS11656J6699595
     [Google Scholar]
  7. WrightF.D. DaileyJ.C. Human bite marks in forensic dentistry.Dent. Clin. North Am.200145236539710.1016/S0011‑8532(22)01767‑011370460
     [Google Scholar]
  8. RaiB. AnandS.C. MadanM. DhattarwalS.K. Bite Marks: A new Identification Technique.Internet Journal of Forensic Science20062110.5580/A6824
     [Google Scholar]
  9. VinayJ. BalajiR.V.S. KumarV.A. ThejaswiniP. Sexual dimorphism in the mandibular canine - A study in South Indian population.Indian J. Forensic Med. Toxicol.201913115510.5958/0973‑9130.2019.00032.X
     [Google Scholar]
  10. ChourasiyaS. Study of odontometric parameters in gender identification.Austin J. Forensic Sci. Criminol.2017422410.26420/austinjforensicscicriminol.2017.1060
     [Google Scholar]
  11. IroanyaO.O. EgwuatuT.F. UgochukwuJ.C. Accuracy of mandibular and maxillary measurements in sex prediction.2018622127
     [Google Scholar]
  12. TarvadiP. ManipadyS. ShettyM. Intercanine distance and bite marks analysis using metric method.Egypt. J. Forensic Sci.20166444544810.1016/j.ejfs.2016.11.001
     [Google Scholar]
  13. AbithaT. SanthanamA. Estadual PaulistaU. Mesquita FilhoJ.D.E. Correlation between bizygomatic and maxillary central incisor width for gender identification.Braz. Dent. Sci.201922445846610.14295/bds.2019.v22i4.1775
     [Google Scholar]
  14. HengD. ManicaS. FrancoA. Forensic dentistry as an analysis tool for sex estimation: A review of current techniques.Res. Rep. Forensic Med. Sci.202212December253910.2147/RRFMS.S334796
     [Google Scholar]
  15. KhatriM. DanielM. SrinivasanS. A comparative study of overlay generation methods in bite mark analysis.J. Forensic Dent. Sci.201351162110.4103/0975‑1475.11455023960410
     [Google Scholar]
  16. OsmanN.A. OmerA.Z. AbuaffanA.H. Comparative study on two methods for bite mark analysis.ARC J. Forensic Sci.20172110.20431/2456‑860049.0201004
     [Google Scholar]
  17. SantoroV. LozitoP. De DonnoA. IntronaF. Experimental study of bite mark injuries by digital analysis.J. Forensic Sci.201156122422810.1111/j.1556‑4029.2010.01519.x20681963
     [Google Scholar]
  18. ThaliM.J. BraunM. MarkwalderT.H. BrueschweilerW. ZollingerU. MalikN.J. YenK. DirnhoferR. Bite mark documentation and analysis: The forensic 3D/CAD supported photogrammetry approach.Forensic Sci. Int.2003135211512110.1016/S0379‑0738(03)00205‑612927412
     [Google Scholar]
  19. ModakR. TamgadgeS. MhapuskarA. HebbaleM. VijayarabhavanN.V. Bite mark analysis: Chasing the bite!Indian J. Oral Health Res.2016226110.4103/2393‑8692.196091
     [Google Scholar]
  20. EvansS.T. JonesC. PlassmannP. 3D imaging for bite mark analysis.Imaging Sci. J.201361435136010.1179/1743131X11Y.0000000054
     [Google Scholar]
  21. FrancoA. PortoL. HengD. MurrayJ. LygateA. FrancoR. BuenoJ. SobaniaM. CostaM.M. ParanhosL.R. ManicaS. AbadeA. Diagnostic performance of convolutional neural networks for dental sexual dimorphism.Sci. Rep.20221211727910.1038/s41598‑022‑21294‑136241670
     [Google Scholar]
  22. KimS. LeeY.H. NohY.K. ParkF.C. AuhQ.S. Age-group determination of living individuals using first molar images based on artificial intelligence.Sci. Rep.2021111107310.1038/s41598‑020‑80182‑833441753
     [Google Scholar]
  23. MahasantipiyaP.M. YeesarapatU. SuriyadetT. SricharoenJ. DumrongwanichA. ThaiupathumpT. Bite mark identification using neural networks: A preliminary study.IMECS 2011 - International MultiConference of Engineers and Computer ScientistsHong Kong, March 16-18, 2011, vol. 1, pp. 16-18.
     [Google Scholar]
/content/journals/cucs/10.2174/0129503779368163250329061333
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Determination of Age and Sex from Human Bite Mark Using Artificial Intelligence
Curr. Comp. Sci. 4, E29503779368163 (2025); https://doi.org/10.2174/0129503779368163250329061333
/content/journals/cucs/10.2174/0129503779368163250329061333
/content/journals/cucs/10.2174/0129503779368163250329061333
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  • Article Type:     Research Article
Keyword(s): age; artificial intelligence; Bite mark; convolutional neural network; forensic science; sex
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