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Automatic gender determination from 3D digital maxillary tooth plaster models based on the random forest algorithm

Betül Akkoç1, Ahmet Arslan2, Hatice Kök3

  • 1Department of Computer Engineering, Faculty of Engineering, Selçuk University, Aleaddin Keykubad Campus, Konya 42075, Turkey.

Computer Methods and Programs in Biomedicine
|April 11, 2017
PubMed
Summary

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This study introduces a smart system for gender determination using 3D digital tooth models. The automated system achieved high accuracy, aiding in forensic identification.

Area of Science:

  • Forensic Odontology
  • Computer Science
  • Biometrics

Background:

  • Gender determination is crucial for individual identification, especially in mass disaster scenarios.
  • Durable dental remains offer a reliable source for identification when other methods fail.
  • 3D digital models of maxillary teeth present a novel approach for forensic analysis.

Purpose of the Study:

  • To develop and evaluate a smart system for automatic gender determination from 3D digital maxillary tooth models.
  • To reduce the search spectrum in forensic identification through automated gender classification.
  • To explore the potential of advanced computational methods in forensic odontology.

Main Methods:

  • Utilized 3D digital plaster models of maxillary teeth from 40 Turkish individuals (20 female, 20 male).
Keywords:
Feature extractionGender determinationImage processingRandom forest algorithm

Related Experiment Videos

  • Employed the iterative closest point (ICP) algorithm for model alignment and segmentation into depth images.
  • Applied local discrete cosine transform (DCT) for feature extraction and random forest (RF) for classification.

    • Main Results:

    • Achieved an average classification accuracy (CA) of 85.166%.
    • Obtained an area under the ROC curve (AUC) of 91.75% using 10-fold cross-validation.
    • Demonstrated the effectiveness of the RF algorithm in gender classification from dental data.

    Conclusions:

    • A multidisciplinary approach integrating computer science, medicine, and dentistry was successfully implemented.
    • The proposed smart system effectively determines gender from 3D digital maxillary tooth models.
    • This research expands the capabilities of gender determination techniques in forensic science using dental evidence.