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Clothing identification via deep learning: forensic applications.

Marianna Bedeli1,2, Zeno Geradts1,2, Erwin van Eijk1,2

  • 1University of Amsterdam, Amsterdam, The Netherlands.

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Summary

This study developed a deep learning model for identifying individuals by classifying clothing attributes. The system achieved over 70% accuracy on logo datasets and surveillance footage, showing promise for forensic applications.

Keywords:
Forensic sciencesattribute identification systemsclothing classificationdeep learningdigital forensiclarge scale datasetsurveillance camera.

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Area of Science:

  • Computer Science
  • Forensic Science
  • Artificial Intelligence

Background:

  • Attribute-based identification systems are crucial for forensic investigations.
  • Clothing serves as a key visual attribute for describing and identifying individuals.
  • Automated identification methods can enhance the efficiency and accuracy of forensic analysis.

Purpose of the Study:

  • To develop and evaluate a deep learning model for identifying individuals based on clothing attributes.
  • To assess the model's performance across different datasets, including large-scale images, logos, and surveillance footage.
  • To determine the effectiveness of clothing classification in real-world forensic scenarios.

Main Methods:

  • Utilized deep learning techniques to train a computer model for clothing image classification.
  • Employed a large-scale dataset for initial model training and evaluation.
  • Tested the model on datasets featuring popular logos and famous brand images.
  • Validated the system's performance using actual surveillance camera footage.

Main Results:

  • The initial model demonstrated relatively poor performance on a large-scale dataset.
  • Clothing classification achieved a success rate exceeding 70% on a dataset of popular logos and brand images.
  • The system performed well on surveillance camera footage, correctly labelling 70% of test images.
  • The study highlights the potential of clothing attribute classification for identification.

Conclusions:

  • Deep learning models can effectively classify clothing attributes for individual identification.
  • The model's accuracy improves with curated datasets, such as those containing logos and brand images.
  • The system shows practical applicability in forensic investigations using surveillance data.
  • Further research can refine the model for enhanced accuracy and broader forensic applications.