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Biometrics: Going 3D.

Gerasimos G Samatas1, George A Papakostas1

  • 1MLV Research Group, Department of Computer Science, International Hellenic University, 65404 Kavala, Greece.

Sensors (Basel, Switzerland)
|September 9, 2022
PubMed
Summary
This summary is machine-generated.

This review analyzes 3D biometrics, focusing on face, hand, and gait identification. It highlights common 3D reconstruction algorithms and suggests future research in multimodal biometrics and anti-spoofing techniques.

Keywords:
3D biometrics3D reconstructioncomputer visionidentity recognition

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Area of Science:

  • Biometrics and Human-Computer Interaction
  • Computer Vision and Image Processing

Background:

  • Biometric identification has evolved from 2D to 3D to enhance reliability.
  • The need for more discriminative features in identification models drove the transition to 3D biometrics.

Purpose of the Study:

  • To provide a comprehensive review of 3D biometrics research published since 2011.
  • To establish the first taxonomy of 3D biometric modalities.
  • To identify current trends, hardware, datasets, and future research directions.

Main Methods:

  • A systematic literature review was conducted using the Scopus database.
  • Identified and categorized 3D biometric modalities, focusing on face, hand, and gait.
  • Analyzed the prevalence of 3D reconstruction algorithms and discussed state-of-the-art techniques.

Main Results:

  • Face (74.07%), hand (20.37%), and gait (5.56%) are the primary 3D biometric categories.
  • Facial and fingerprint biometrics are predominant within their respective categories.
  • Stereo vision and structure-from-silhouette (SfS) are the most frequently used 3D reconstruction algorithms (51%).

Conclusions:

  • Future research should focus on multimodal biometrics, 3D reconstruction algorithm generalization, and anti-spoofing metrics.
  • Development of integrated 2D/3D biometric devices and increased availability of public datasets are recommended.