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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
Summary
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.
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.
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