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Likelihood Ratios for Deep Neural Networks in Face Comparison.

Andrea Macarulla Rodriguez1, Zeno Geradts1, Marcel Worring2

  • 1Netherlands Forensic Institute, Laan van Ypenburg 6, 2497 GB, Den Haag, The Netherlands.

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|May 13, 2020
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Summary
This summary is machine-generated.

Automated facial comparison systems show high accuracy for low-quality images, outperforming human forensic experts in detecting non-matches. These systems can assist experts with faster, reliable comparisons, especially for full-frontal images.

Keywords:
ENFSI proficiency testdeep learningdigital forensic scienceface recognitionface verification likelihood ratio

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Area of Science:

  • Forensic Science
  • Computer Vision
  • Biometrics

Background:

  • Forensic facial comparison is crucial for legal proceedings.
  • Transparency and open-source methods are vital in forensic science.
  • Automated systems offer potential support to human experts.

Purpose of the Study:

  • To compare the performance of automated facial comparison systems and human forensic experts.
  • To evaluate the utility of machine learning in supporting courtroom evidence.
  • To assess likelihood ratio computation for facial comparisons.

Main Methods:

  • Utilized three open-source convolutional neural network systems: OpenFace, SeetaFace, and FaceNet.
  • Converted system outputs (distance/similarity) to likelihood ratios using Weibull distribution, kernel density estimation, and isotonic regression.
  • Compared system performance against forensic investigators using low- and good-quality frontal images.

Main Results:

  • Automated systems achieved 100% precision and specificity for non-match detection with low-quality images, surpassing investigators (89% and 86%).
  • Forensic experts performed better with good-quality images.
  • A rank correlation of approximately 80% was observed between investigators and software performance.

Conclusions:

  • Open-source facial comparison software can assist forensic reporting officers with faster, reliable comparisons, particularly for full-frontal images.
  • Automated systems demonstrate potential as a supportive tool for human experts in forensic casework.
  • The study highlights the complementary roles of automated systems and human expertise in facial identification.