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Deep Convolutional Neural Network Used in Single Sample per Person Face Recognition
Junying Zeng1, Xiaoxiao Zhao1, Junying Gan1
1School of Information Engineering, Wuyi University, Jiangmen 529020, China.
Computational Intelligence and Neuroscience
|September 14, 2018
Summary
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.
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.
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