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A Study of Deep Learning-Based Face Recognition Models for Sibling Identification.

Rita Goel1, Irfan Mehmood1, Hassan Ugail1

  • 1Centre of Visual Computing, University of Bradford, Bradford BD7 1DP, UK.

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|August 10, 2021
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Summary
This summary is machine-generated.

Identifying siblings using deep learning face recognition is hard due to facial similarities. This study shows VGGFace excels at full-face and eye comparisons (over 95% accuracy), while FaceNet is best for nose identification.

Keywords:
FaceNetVGG16VGG19VGGFaceface recognitionsibling recognition

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Area of Science:

  • Computer Science
  • Biometrics
  • Artificial Intelligence

Background:

  • Sibling identification via face recognition presents unique challenges due to high inter-sibling facial similarity.
  • Deep learning models offer advanced capabilities for analyzing subtle facial features.

Purpose of the Study:

  • To evaluate the efficacy of state-of-the-art deep learning models in discriminating between sibling faces.
  • To compare the performance of FaceNet, VGGFace, VGG16, and VGG19 using various similarity indices.

Main Methods:

  • Deep learning embeddings were generated for sibling image pairs using FaceNet, VGGFace, VGG16, and VGG19.
  • Five similarity measures (cosine, Euclidean, structured, Manhattan, Minkowski) were applied.
  • Performance was assessed using accuracy, precision, and misclassification rates derived from confusion matrices on four facial regions (full-face, eyes, nose, forehead).

Main Results:

  • Model performance varied significantly across different facial regions.
  • VGGFace achieved over 95% accuracy for full-frontal-face and eye comparisons.
  • FaceNet demonstrated superior performance for nose-based classification, while VGG16 and VGG19 were effective for forehead comparisons.

Conclusions:

  • Deep learning models show potential for sibling identification, with performance dependent on the specific model and facial region analyzed.
  • VGGFace and FaceNet show promise for specific sibling identification tasks based on facial features.