Abstract

Aim: The pinna, the hearing organ, also contributes to the aesthetic appearance of the face. We aimed to investigate the feasibility of sex prediction using anthropometric measurements of the pinna in machine learning.

Methods: The study included two hundred healthy individuals (104 women and 96 men). The pinna of these individuals were measured in eight different parts using a digital calliper. The data, which differed by sex, were processed in eight different machine-learning algorithms.

Results: Seven different measurements, such as pinna length, width and lobule length, were greater in men than in women (p<0.05). The K-Nearest Neighbor model showed the best success in sex prediction with an accuracy of 0.825 and a ROC value of 0.882.

Conclusions: Pinna's anthropometric measurement values can be used in machine learning to predict sex with a high success rate. Our study shows that ear prints may have potential use in forensic identification.

Keywords: anthropometry, machine learning, pinna, sex estimation

Copyright and license

Copyright © 2025 The Author(s). This is an open-access article published by Bolu İzzet Baysal Training and Research Hospital under the terms of the Creative Commons Attribution License (CC BY) which permits unrestricted use, distribution, and reproduction in any medium or format, provided the original work is properly cited.

How to cite

1.
Söylemez E, Tokgöz Yılmaz S. Pinna anthropometry in sex estimation: a machine learning-based approach. Northwestern Med J. 2025;5(2):77-84. https://doi.org/10.54307/2025.NWMJ.120

References

  1. Kumar BS, Selvi GP. Morphometry of ear pinna in sex determination. International Journal of Anatomy and Research. 2016; 4(2): 24802484. https://doi.org/10.16965/ijar.2016.244
  2. Boesoirie SF, Handayani R, Gatera VA, Aroeman NA, Boesoirie TS. Determination of the Difference Between Men and Women Anthropometry Auricles Using Photogrammetric Method in Sundanese Ethnic Group. Clin Cosmet Investig Dermatol. 2022; 15: 2133-41. https://doi.org/10.2147/CCID.S380115
  3. He Y, Xue GH, Fu JZ. Fabrication of low cost soft tissue prostheses with the desktop 3D printer. Sci Rep. 2014; 4: 6973. https://doi.org/10.1038/srep06973
  4. Açar G. Digitalized analysis of the external ear morphometry and correlation with stature, gender and body mass index in young adults. Journal of Global Health & Natural Science. 2021; 4(1): 12-22.
  5. Sharma A, Sidhu NK, Sharma MK, Kapoor K, Singh B. Morphometric study of ear lobule in northwest Indian male subjects. Anat Sci Int. 2007; 82(2): 98-104. https://doi.org/10.1111/j.1447-073X.2007.00166.x
  6. Özmen Akyol S, Sezgin N. Investigation of the Effect of Ear Measurements on Sex Estimation in Forensic Sciences Using Machine Learning Techniques: Descriptive Research. Turkiye Klinikleri J Foren Sci Leg Med. 2023; 20(3): 161-72. https://doi.org/10.5336/forensic.2023-96327
  7. Meijerman L, Sholl S, De Conti F, et al. Exploratory study on classification and individualisation of earprints. Forensic Sci Int. 2004; 140(1): 91-9. https://doi.org/10.1016/j.forsciint.2003.10.024
  8. Bozkir MG, Karakaş P, Yavuz M, Dere F. Morphometry of the external ear in our adult population. Aesthetic Plast Surg. 2006; 30(1): 81-5. https://doi.org/10.1007/s00266-005-6095-1
  9. Kalcioglu MT, Miman MC, Toplu Y, Yakinci C, Ozturan O. Anthropometric growth study of normal human auricle. Int J Pediatr Otorhinolaryngol. 2003; 67(11): 1169-77. https://doi.org/10.1016/s0165-5876(03)00221-0
  10. Petrescu L, Gheorghe A, Ionescu Tîrgovışte C, Dumıtru-Petrescu C. Anthropometric investigation of external ear morphology, as a pattern of uniqueness, useful in identifying the person. Proc Rom Acad. 2018; 20(2): 95-104. https://doi.org/10.3844/jmrsp.2018.85.104
  11. Rani D, Krishan K, Sahani R, Baryah N, Kanchan T. Variability in human external ear anthropometry- Anthropological and forensic applications. Clin Ter. 2022; 172(6): 531-41.
  12. Han K, Kwon HJ, Choi TH, Kim JH, Son D. Comparison of anthropometry with photogrammetry based on a standardized clinical photographic technique using a cephalostat and chair. J Craniomaxillofac Surg. 2010; 38(2): 96-107. https://doi.org/10.1016/j.jcms.2009.04.003
  13. Laxman K. A Study of Determination of Stature in Hyderabad Population from External Ear Morphometry. Medico-legal Update. 2009; 19(1): 164-8. https://doi.org/10.5958/0974-1283.2019.00033.1
  14. Chantajitr S, Wattanawong K. Anthropometric Study of the Normal External Ear in Adult Thai People; Age and Sex-Related Difference. J Med Assoc Thai. 2021; 104: 61-9. https://doi.org/10.35755/jmedassocthai.2021.S05.00074
  15. Ito I, Imada M, Ikeda M, Sueno K, Arikuni T, Kida A. A morphological study of age changes in adult human auricular cartilage with special emphasis on elastic fibers. Laryngoscope. 2001; 111(5): 881-6. https://doi.org/10.1097/00005537-200105000-00023
  16. Sezgin N, Ersoy G. Metric and morphological features of the ear in sex classification. Egyptian Journal of Forensic Sciences. 2023; 13: 44. https://doi.org/10.1186/s41935-023-00364-z
  17. Murgod V, Angadi P, Hallikerimath S, et al. Anthropometric study of the external ear and its applicability in sex identification: assessed in an Indian sample. Australian Journal of Forensic Sciences. 2013; 45: 431-44. https://doi.org/10.1080/00450618.2013.767374
  18. Gou J, Zhan Y, Rao Y, Shen X, Wang X, He Wl. Improved pseudo nearest neighbor classification. Knowledge-Based Systems. 2014; 70: 361-75. https://doi.org/10.1016/j.knosys.2014.07.020
  19. Prasetyo AT, Putri IL. Anthropometric Study of Human Ear: A Baseline Data for Ear Reconstruction. J Craniofac Surg. 2022; 33(4): 1245-9. https://doi.org/10.1097/SCS.0000000000008199