A Review of Some Recent Advances in the Use of Cheiloscopy and Dermatoglyphics for Forensic Investigations
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487-498
Published:
Aug 20, 2025
Digital Object Identifier:
10.58578/ajmsphr.v2i3.7233
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Authors:
Mosugu O. O.1, Otashu K. F.2, Salman J. I.3, Nsisong S. W.4, Alfred A. W.5, Ajayi S. O.6, Bright C. E.7, Jibaniya G. M.8, Katchin E. S.9, Tongle N. J.10 
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Main Article Content
Abstract
The field of forensic investigation has advanced significantly, particularly in developed countries, with new technologies enhancing the reliability of human identification. This review highlights recent innovations in lip printing (cheiloscopy) and fingerprinting (dermatoglyphics), focusing on their application in forensic science. While dermatoglyphics remains a conventional method, cheiloscopy has emerged as a complementary, less conventional tool for investigation and research. Recent developments incorporate artificial intelligence (AI) and machine learning (ML) techniques, which have improved the accuracy and efficiency of forensic analyses. Multimodal biometric systems that integrate cheiloscopy and dermatoglyphics further reduce error rates and increase reliability, offering stronger fraud resistance. Despite these advancements, many developing countries have yet to fully adopt or master AI- and ML-based forensic tools, limiting their application in real-world investigations. The review concludes that integrating these technologies into forensic practice has the potential to significantly improve human identification, though challenges related to accessibility, expertise, and infrastructure must be addressed.
Article Details
How to Cite
O., M. O., F., O. K., I., S. J., W., N. S., W., A. A., O., A. S., E., B. C., M., J. G., S., K. E., & J., T. N. (2025). A Review of Some Recent Advances in the Use of Cheiloscopy and Dermatoglyphics for Forensic Investigations. African Journal of Medicine, Surgery and Public Health Research, 2(3), 487-498. https://doi.org/10.58578/ajmsphr.v2i3.7233
References
1 Abedi, M., Afoakwah, C., and Bonsu, D. N. O. (2020). Lip Print Enhancement: Review. Forensic Sciences Research, 7 (1), 24–28.
2 Abidullah, M. (2023). Correlation among lip print pattern, fingerprint pattern and ABO blood group. Journal of Pharmacy and Bio Allied Sciences, 15(1), S27-S30.
3 Bandyopadhyay, S.K., Arunkumar, S., and Bhattacharjee, S. (2013). Feature extraction of human lip prints. arXiv preprint arXiv:1312.0852. https://arxiv.org/abs/1312.0852
4 Caldas, I. M., Magalhães, T., and Afonso, A. (2007). Establishing identity using cheiloscopy and palatoscopy. Forensic Science International, 165(1), 1–9.
5 Caputo, I. G. C., Antonio, L. U., André, A. P. R., de Castro, M. G., Pinto, L. B., da Cunha, R. D., and Carvalho, M. S. O. A. (2018). Cheiloscopy in the human identification. Forensic Research and Criminology International Journal, 6(6), 371–374.
6 Champod, C., Lennard, C., Margot, P., & Stoilovic, M. (2004). Fingerprints and other ridge skin impressions. CRC Press.
7 Clove Dental. (2023). Cheiloscopy: A crucial technique in forensic science for personal identification. Retrieved from https://www.clovedental.in/blogs/cheiloscopy-a-crucial-technique-in-forensic-science/
8 Cohen, P. R., Abdulkarim, B., Wnuk, M., Sutton, L., & Others. (2024). Identification of decedents by restoring mummified fingerprints: Forensic dermatology in the investigation of mummy dermatoglyphics. Clinics in Dermatology. https://www.sciencedirect.com/science/article/pii/S0738081X24001858
9 Di Vita, E., Cacioppo, A., Sciarra, F. M., Messina, P., Cumbo, E. M., Caivano, G., Zerbo, S., Argo, A., Albano, G. D., and Scardina, G. A. (2025). Preliminary insights into 3D cheiloscopy for forensic applications: A pilot study. Applied Sciences, 15(4), 1726
10 Farrukh, W., and Van der Haar, D. (2023). Lip print-based identification using traditional and deep learning. IET Biometrics, 12(1), 1–12.
11 Han, Q., Yang, H., Weng, T., Chen, G., Liu, J., and Tian, Y. (2021). Multimodal identification based on fingerprint and face images via a hetero-associative memory method. Mathematics, 9(22), 2976.
12 Hawkeye Forensic. (2023). Cheiloscopy: Role in forensic investigations. Retrieved from https://hawkeyeforensic.com/2023/11/24/cheiloscopy-role-in-forensic-investigations/
13 Jain, J., Khanapure, S. C., Ananda, S. R., Supreetha, S., Abhishek, K. N., and Shilpa, M. (2020). Reliability of digitized cheiloscopy, dactyloscopy, and combination approach in gender identification. Annals of Maxillofacial Research, 10(1), 5–10
14 Kalburgi, S., Lomte, V. M., Jain, N., Buge, A., and Kankal, S. (2023). A survey on lip reading recognition using artificial intelligence. International Journal of Creative Research Thoughts (IJCRT), 11(11).
15 Khapra, P., Agarwal, S., and Sharma, U. (2024). Deep learning for lip reading and speech recognition. International Journal of Science, Engineering and Technology, 12(1), 575–580.
16 Kumar, S., & Singla, R. (2015). A machine learning approach for dermatoglyphics analysis. International Journal of Computer Applications, 121(1), 6–12.
17 Mahapatra, N., Behura, S. S., Bhuyan, L., & Patra, M. (2024). Gender determination from ridges using dermatoglyphics techniques. Journal of Forensic Medicine and Toxicology. https://pmc.ncbi.nlm.nih.gov/articles/PMC11795458/
18 Maheswari, T. N. U., Bohra, A., and Sharuniveda, S. (2024). Pioneering a new frontier: Artificial intelligence (AI)-driven lip print pattern analysis—A systematic review. Journal of Indian Academy of Oral Medicine and Radiology, 36(3), 206–212.
19 Maltoni, D., Maio, D., Jain, A. K., & Prabhakar, S. (2003). Handbook of fingerprint recognition. Springer.
20 Palakurthi, N., Priyanka, G. V., Narayen, V., Sowmya, P., and Veenila. (2023). Cheiloscopy: Scope in forensics, classification systems and limitations. International Journal of Novel Research and Development, 8(4), 572–578.
21 Palakurthi, S., Reddy, P. S., and Kumar, V. (2023). Comparative analysis of machine learning algorithms for lip print-based person identification. International Journal of Computer Applications, 182(1), 1–5.
22 Rizwan, M., Qazi, R., & Farooq, M. (2014). Dermatoglyphics in schizophrenia: A review. International Journal of Research in Medical Sciences, 2(4), 1236–1241.
23 Sandhya, D., and Daware, S. (2012). A study of digital forensics: Process and tools. In Proceedings of the 2nd National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2013) (pp. 29–31). Foundation of Computer Science (FCS), NY, USA. https://ijcaonline.org/proceedings/ncipet/number10/5267-1079/
24 Sandhya, S., Fernandes, R., Sapna, S., and Rodrigues, A. P. (2022). Comparative analysis of machine learning algorithms for lip print-based person identification. Evolutionary Intelligence. https://doi.org/10.1007/s12065-022-00775-7:
25 Sankaran, A., Alhindi, T., Ross, A., & Ho, J. (2020). Enhancing the quality of latent fingerprints using generative adversarial networks. IEEE Transactions on Information Forensics and Security, 15, 3640–3655. https://doi.org/10.1109/TIFS.2020.2990944
26 Sen, S., Ghosh, K., Chattopadhyay, J., Bhattacharya, M., Bose, S., and Shetty, R. (2023). Cheiloscopy: A diagnostic aid in forensic science to differentiate between smokers and non-smokers. IOSR Journal of Dental and Medical Sciences, 22(11), 25–30.
27 Sen, S., Rani, M., Dillon, D. J., and Sunil. (2023). Lip print analysis: A study on patterns and forensic applications. Indian Journal of Forensic Medicine and Toxicology, 18(4), 69-74.
28 Sharma, P., Sharma, N., Wadhwan, V., and Aggarwal, P. (2016). Can lip prints provide biological evidence? Journal of Forensic Dental Sciences, 8(3), 175-178.
29 Singh, M., Singh, R., and Ross, A. (2019). A comprehensive overview of biometric fusion. arXiv preprint arXiv:1902.02919. https://arxiv.org/abs/1902.02919
30 Sundquist, T. (2017). DNA evidence from lip prints and used cups: Interesting cases from the International Symposium on Human Identification. Part I. ISHI News. https://www.ishinews.com/dna-evidence-from-lip-prints-and-used-cups-interesting-cases-from-the-international-symposium-on-human-identification-part-i/
31 Sawhney, T., Sharma, A., Abrol, P., & Lehana, P. K. (2025). Fingerprint matching for noisy and distorted patterns using a Siamese network with ResNet50 and multihead attention. IEEE Transactions on Computational Intelligence and AI in Forensics. https://ieeexplore.ieee.org/abstract/document/10988862
32 Zhao, H., Li, C., & Lin, J. (2022). Edge-AI framework for real-time biometric authentication in mobile forensic systems. Forensic Science International: Digital Investigation, 41, 301120. https://doi.org/10.1016/j.fsidi.2022.301120
2 Abidullah, M. (2023). Correlation among lip print pattern, fingerprint pattern and ABO blood group. Journal of Pharmacy and Bio Allied Sciences, 15(1), S27-S30.
3 Bandyopadhyay, S.K., Arunkumar, S., and Bhattacharjee, S. (2013). Feature extraction of human lip prints. arXiv preprint arXiv:1312.0852. https://arxiv.org/abs/1312.0852
4 Caldas, I. M., Magalhães, T., and Afonso, A. (2007). Establishing identity using cheiloscopy and palatoscopy. Forensic Science International, 165(1), 1–9.
5 Caputo, I. G. C., Antonio, L. U., André, A. P. R., de Castro, M. G., Pinto, L. B., da Cunha, R. D., and Carvalho, M. S. O. A. (2018). Cheiloscopy in the human identification. Forensic Research and Criminology International Journal, 6(6), 371–374.
6 Champod, C., Lennard, C., Margot, P., & Stoilovic, M. (2004). Fingerprints and other ridge skin impressions. CRC Press.
7 Clove Dental. (2023). Cheiloscopy: A crucial technique in forensic science for personal identification. Retrieved from https://www.clovedental.in/blogs/cheiloscopy-a-crucial-technique-in-forensic-science/
8 Cohen, P. R., Abdulkarim, B., Wnuk, M., Sutton, L., & Others. (2024). Identification of decedents by restoring mummified fingerprints: Forensic dermatology in the investigation of mummy dermatoglyphics. Clinics in Dermatology. https://www.sciencedirect.com/science/article/pii/S0738081X24001858
9 Di Vita, E., Cacioppo, A., Sciarra, F. M., Messina, P., Cumbo, E. M., Caivano, G., Zerbo, S., Argo, A., Albano, G. D., and Scardina, G. A. (2025). Preliminary insights into 3D cheiloscopy for forensic applications: A pilot study. Applied Sciences, 15(4), 1726
10 Farrukh, W., and Van der Haar, D. (2023). Lip print-based identification using traditional and deep learning. IET Biometrics, 12(1), 1–12.
11 Han, Q., Yang, H., Weng, T., Chen, G., Liu, J., and Tian, Y. (2021). Multimodal identification based on fingerprint and face images via a hetero-associative memory method. Mathematics, 9(22), 2976.
12 Hawkeye Forensic. (2023). Cheiloscopy: Role in forensic investigations. Retrieved from https://hawkeyeforensic.com/2023/11/24/cheiloscopy-role-in-forensic-investigations/
13 Jain, J., Khanapure, S. C., Ananda, S. R., Supreetha, S., Abhishek, K. N., and Shilpa, M. (2020). Reliability of digitized cheiloscopy, dactyloscopy, and combination approach in gender identification. Annals of Maxillofacial Research, 10(1), 5–10
14 Kalburgi, S., Lomte, V. M., Jain, N., Buge, A., and Kankal, S. (2023). A survey on lip reading recognition using artificial intelligence. International Journal of Creative Research Thoughts (IJCRT), 11(11).
15 Khapra, P., Agarwal, S., and Sharma, U. (2024). Deep learning for lip reading and speech recognition. International Journal of Science, Engineering and Technology, 12(1), 575–580.
16 Kumar, S., & Singla, R. (2015). A machine learning approach for dermatoglyphics analysis. International Journal of Computer Applications, 121(1), 6–12.
17 Mahapatra, N., Behura, S. S., Bhuyan, L., & Patra, M. (2024). Gender determination from ridges using dermatoglyphics techniques. Journal of Forensic Medicine and Toxicology. https://pmc.ncbi.nlm.nih.gov/articles/PMC11795458/
18 Maheswari, T. N. U., Bohra, A., and Sharuniveda, S. (2024). Pioneering a new frontier: Artificial intelligence (AI)-driven lip print pattern analysis—A systematic review. Journal of Indian Academy of Oral Medicine and Radiology, 36(3), 206–212.
19 Maltoni, D., Maio, D., Jain, A. K., & Prabhakar, S. (2003). Handbook of fingerprint recognition. Springer.
20 Palakurthi, N., Priyanka, G. V., Narayen, V., Sowmya, P., and Veenila. (2023). Cheiloscopy: Scope in forensics, classification systems and limitations. International Journal of Novel Research and Development, 8(4), 572–578.
21 Palakurthi, S., Reddy, P. S., and Kumar, V. (2023). Comparative analysis of machine learning algorithms for lip print-based person identification. International Journal of Computer Applications, 182(1), 1–5.
22 Rizwan, M., Qazi, R., & Farooq, M. (2014). Dermatoglyphics in schizophrenia: A review. International Journal of Research in Medical Sciences, 2(4), 1236–1241.
23 Sandhya, D., and Daware, S. (2012). A study of digital forensics: Process and tools. In Proceedings of the 2nd National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2013) (pp. 29–31). Foundation of Computer Science (FCS), NY, USA. https://ijcaonline.org/proceedings/ncipet/number10/5267-1079/
24 Sandhya, S., Fernandes, R., Sapna, S., and Rodrigues, A. P. (2022). Comparative analysis of machine learning algorithms for lip print-based person identification. Evolutionary Intelligence. https://doi.org/10.1007/s12065-022-00775-7:
25 Sankaran, A., Alhindi, T., Ross, A., & Ho, J. (2020). Enhancing the quality of latent fingerprints using generative adversarial networks. IEEE Transactions on Information Forensics and Security, 15, 3640–3655. https://doi.org/10.1109/TIFS.2020.2990944
26 Sen, S., Ghosh, K., Chattopadhyay, J., Bhattacharya, M., Bose, S., and Shetty, R. (2023). Cheiloscopy: A diagnostic aid in forensic science to differentiate between smokers and non-smokers. IOSR Journal of Dental and Medical Sciences, 22(11), 25–30.
27 Sen, S., Rani, M., Dillon, D. J., and Sunil. (2023). Lip print analysis: A study on patterns and forensic applications. Indian Journal of Forensic Medicine and Toxicology, 18(4), 69-74.
28 Sharma, P., Sharma, N., Wadhwan, V., and Aggarwal, P. (2016). Can lip prints provide biological evidence? Journal of Forensic Dental Sciences, 8(3), 175-178.
29 Singh, M., Singh, R., and Ross, A. (2019). A comprehensive overview of biometric fusion. arXiv preprint arXiv:1902.02919. https://arxiv.org/abs/1902.02919
30 Sundquist, T. (2017). DNA evidence from lip prints and used cups: Interesting cases from the International Symposium on Human Identification. Part I. ISHI News. https://www.ishinews.com/dna-evidence-from-lip-prints-and-used-cups-interesting-cases-from-the-international-symposium-on-human-identification-part-i/
31 Sawhney, T., Sharma, A., Abrol, P., & Lehana, P. K. (2025). Fingerprint matching for noisy and distorted patterns using a Siamese network with ResNet50 and multihead attention. IEEE Transactions on Computational Intelligence and AI in Forensics. https://ieeexplore.ieee.org/abstract/document/10988862
32 Zhao, H., Li, C., & Lin, J. (2022). Edge-AI framework for real-time biometric authentication in mobile forensic systems. Forensic Science International: Digital Investigation, 41, 301120. https://doi.org/10.1016/j.fsidi.2022.301120