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Deep learning-based CNN model for multiclass classification of fingerprint patterns.

Apurav Mahajan1, Damini Siwan2, Peehul Krishan3

  • 1Department of Anthropology, Panjab University, Chandigarh, India.

Medicine, Science, and the Law
|July 4, 2025
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Summary
This summary is machine-generated.

This study introduces an artificial intelligence model for classifying fingerprints. The convolutional neural network (CNN) achieved high accuracy, aiding in faster fingerprint analysis for forensic applications.

Keywords:
Forensic scienceartificial intelligenceconvolutional neural networkcrime scene investigationfingerprint classificationforensic case-work and research

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Area of Science:

  • Biometrics
  • Artificial Intelligence
  • Forensic Science

Background:

  • Fingerprints are unique biometric identifiers used globally for identification and security.
  • Manual fingerprint classification is time-consuming and requires expertise.
  • Automated systems can enhance efficiency in fingerprint matching and crime scene analysis.

Purpose of the Study:

  • To develop and evaluate a convolutional neural network (CNN) model for multiclass fingerprint pattern classification.
  • To classify fingerprints into Henry's categories: Arches, Loops, Whorls, and Composites.
  • To assess the CNN model's performance for aiding forensic examinations.

Main Methods:

  • A convolutional neural network (CNN) model was designed for multiclass fingerprint classification.
  • The model was trained on a dataset of 2000 fingerprint patterns from 200 participants.
  • The dataset was divided into training, testing, and validation sets (8:1:1 ratio).

Main Results:

  • The CNN model achieved training accuracy of 89%, validation accuracy of 84%, and testing accuracy of 85.5%.
  • Performance was evaluated using a confusion matrix for the testing dataset.
  • The model demonstrated effective classification across the four main fingerprint patterns.

Conclusions:

  • The developed CNN model offers a reliable automated tool for fingerprint classification.
  • This AI-driven approach can significantly improve the speed and accuracy of fingerprint analysis in forensic investigations.
  • The model supports fingerprint research and crime scene analysis by providing rapid classification.