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  1. Home
  3. Convolutional Neural Network Based Children Recognition System Using Contactless Fingerprints.
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  3. Convolutional Neural Network Based Children Recognition System Using Contactless Fingerprints.

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Convolutional neural network based children recognition system using contactless fingerprints.

Kanchana Rajaram1, N G Bhuvaneswari Amma2, S Selvakumar3,4

  • 1Department of Computer Science and Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, Tamil Nadu 603 110 India.

International Journal of Information Technology : an Official Journal of Bharati Vidyapeeth'S Institute of Computer Applications and Management
|June 26, 2023

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces Child-CLEF, a contactless fingerprint recognition system for children using Convolutional Neural Networks (CNNs). The system enhances image quality and extracts features for accurate identification, outperforming existing methods.

Keywords:
Biometric securityChild fingerprintsContactless fingerprintsConvolutional neural networkFingerprint identificationImage acquisition

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Area of Science:

  • Biometrics and Pattern Recognition
  • Computer Vision and Image Processing
  • Artificial Intelligence in Security

Background:

  • Fingerprint recognition is crucial for security, but challenges exist with children's immature fingerprints and difficult image acquisition.
  • The COVID-19 pandemic highlighted the need for contactless biometric solutions, especially for children.
  • Existing systems struggle with the unique challenges of pediatric fingerprint data.

Purpose of the Study:

  • To develop a robust and accurate contactless fingerprint recognition system for children.
  • To address the limitations of traditional fingerprint recognition methods for pediatric biometrics.
  • To leverage deep learning for enhanced children's fingerprint identification.

Main Methods:

  • A Convolutional Neural Network (CNN) based system, Child-CLEF, was developed using a Contact-Less Children Fingerprint (CLCF) dataset.
  • A hybrid image enhancement technique was employed to improve the quality of acquired fingerprint images.
  • Minutiae features were extracted using the Child-CLEF Net model, followed by identification via a matching algorithm.

    • Main Results:

    • The Child-CLEF system demonstrated superior performance compared to existing fingerprint recognition systems.
    • The system achieved high accuracy and a low equal error rate (EER) on both self-captured and public datasets.
    • Contactless acquisition using mobile phones proved effective for children's fingerprints.

    Conclusions:

    • The proposed Child-CLEF system offers a promising solution for contactless children's fingerprint recognition.
    • The study validates the effectiveness of CNNs and image enhancement for pediatric biometric identification.
    • This research contributes to secure and non-invasive identification methods for children, particularly in sensitive contexts.