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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Adaptive Spatial Transformation Networks for Periocular Recognition.

Diana Laura Borza1, Ehsan Yaghoubi2, Simone Frintrop2

  • 1Informatics Department, Faculty of Mathematics and Informatics, Babes Bolyai University, 1st Mihail Kogalniceanu Street, 400084 Cluj-Napoca, Romania.

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|March 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning framework for periocular recognition, enhancing accuracy in challenging conditions like mask-wearing. The method uses local branches to analyze discriminative facial areas, improving identification performance.

Keywords:
attentionbiometricsperiocular recognitionspatial transform

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Area of Science:

  • Computer Vision
  • Biometrics
  • Deep Learning

Background:

  • Periocular recognition is crucial for biometrics, especially with face occlusions (e.g., COVID-19 masks).
  • Traditional face recognition fails when faces are partially obscured.
  • The periocular region offers unique identification cues.

Purpose of the Study:

  • To develop a deep learning framework for robust periocular recognition.
  • To automatically localize and analyze discriminative areas within the periocular region.
  • To improve identification accuracy in challenging, occluded scenarios.

Main Methods:

  • A novel deep learning architecture with parallel local branches for feature analysis.
  • Semi-supervised learning to identify discriminative areas in feature maps.
  • Spatial transformation matrices for region of interest selection (cropping, scaling).
  • Fusion of information from local branches and a global branch for final recognition.

Main Results:

  • Consistent improvement in mean Average Precision (mAP) by over 4% when integrated with ResNet architectures on the UBIRIS-v2 benchmark.
  • Ablation studies confirmed the positive influence of spatial transformation and local branches on performance.
  • The proposed framework demonstrates enhanced accuracy over baseline architectures.

Conclusions:

  • The developed periocular recognition framework offers a significant advancement in biometric identification under occlusion.
  • The modular design with local branches enhances the model's ability to focus on discriminative periocular features.
  • The method shows adaptability to other computer vision tasks, highlighting its versatility.