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Sex classification accuracy through machine learning algorithms - morphometric variables of human ear and nose.

Tej Kaur1, Kewal Krishan2, Akanksha Sharma1

  • 1Institute of Forensic Science and Criminology, Panjab University, Sector-14, Chandigarh, India.

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Summary

This study used machine learning to predict sex from ear and nose measurements, achieving 86.75% accuracy. Nasal breadth was the most significant predictor for personal identification in forensic science.

Keywords:
Ear and nose morphologyForensic identificationHuman anatomy and morphologyMachine learning algorithmsPyCaretSex classification

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Area of Science:

  • Forensic anthropology
  • Biometrics
  • Machine learning applications

Background:

  • Accurate sex determination is crucial for personal identification in forensic and medico-legal contexts.
  • Facial features, specifically ear and nose morphology, offer potential biometric markers for sex estimation.
  • Traditional methods may have limitations, necessitating novel approaches.

Purpose of the Study:

  • To develop and evaluate a machine learning model for accurate sex prediction using ear and nose measurements.
  • To identify the most significant facial parameters contributing to sex determination.
  • To assess the efficacy of the PyCaret library in forensic sex classification.

Main Methods:

  • A dataset of 508 participants (North India, aged 18-35) with recorded ear and nose measurements was utilized.
  • The PyCaret machine learning library was employed, implementing a train-evaluate-test validation approach.
  • Logistic regression was identified as the top-performing classifier after comparing multiple models based on accuracy and computational time.

Main Results:

  • The logistic regression model achieved a sex prediction accuracy of 86.75%.
  • Nasal breadth was identified as the most significant variable for accurate sex prediction.
  • Most ear and nose measurements demonstrated significant contributions to sexual dimorphism.

Conclusions:

  • Machine learning, specifically using PyCaret, offers a highly effective method for sex determination from craniofacial anthropometry.
  • Nasal breadth is a key biometric indicator for sex estimation in forensic investigations.
  • This approach can enhance personal identification in forensic examinations and crime scene investigations.