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Deep Convolutional Neural Network Used in Single Sample per Person Face Recognition.

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This study introduces a novel hybrid approach combining traditional and deep learning (TDL) for single sample per person face recognition. TDL significantly enhances accuracy in challenging face recognition scenarios.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence

Background:

  • Single Sample Per Person (SSPP) face recognition is challenging due to limited training data.
  • Facial variations like pose, illumination, and disguise complicate accurate identification.

Purpose of the Study:

  • To propose an effective method for SSPP face recognition.
  • To address the limitations of traditional methods in handling facial variations.

Main Methods:

  • Developed a novel expanding sample method based on traditional approaches for generating diverse facial samples (disguise, expression, mixed variations).
  • Utilized transfer learning by fine-tuning a pre-trained Deep Convolutional Neural Network (DCNN) model with the generated expanding samples.
  • Implemented and evaluated the proposed Traditional and Deep Learning (TDL) scheme.

Main Results:

  • The TDL method demonstrated superior performance in SSPP face recognition tasks.
  • Achieved state-of-the-art results across multiple benchmark face databases (AR, Extend Yale B, FERET, LFW).

Conclusions:

  • The proposed TDL scheme effectively overcomes the challenges of SSPP face recognition.
  • The hybrid approach offers a robust and convenient solution for accurate facial identification with limited samples.