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Lightweight and Resource-Constrained Learning Network for Face Recognition with Performance Optimization.

Hsiao-Chi Li1, Zong-Yue Deng2, Hsin-Han Chiang2

  • 1Department of Computer Science and Information Engineering, Fu Jen Catholic University, No. 510, Zhongzheng Road, Xinzhuang District, New Taipei City 242, Taiwan.

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Summary
This summary is machine-generated.

This study introduces FN13, a lightweight face recognition model that overcomes hardware limitations. It achieves high accuracy with fewer parameters and reference images, making efficient AI deployment possible.

Keywords:
FaceNetdeep convolutional networkface recognitionlightweight optimizationresource constraintssurveillance system

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep learning (DL) and convolutional neural networks (CNNs) have advanced face recognition.
  • FaceNet improves accuracy but demands significant computational resources, limiting field deployment.
  • Security applications require lightweight and efficient face recognition for edge devices.

Purpose of the Study:

  • To develop a lightweight face recognition network (FN13) based on FaceNet.
  • To address hardware limitations in computational resources for edge devices.
  • To improve efficiency and reduce model complexity while maintaining high accuracy.

Main Methods:

  • Proposed FN13, a lightweight learning network improved from FaceNet.
  • Utilized center loss to minimize between-class feature variations and maximize within-class feature differences.
  • Reduced the number of parameters compared to the original FaceNet model.

Main Results:

  • FN13 requires fewer grayscale reference images per subject.
  • The model maintains a high degree of accuracy despite reduced parameters.
  • Demonstrated validity on the Labeled Faces in the Wild (LFW) dataset.

Conclusions:

  • FN13 offers an efficient solution for face recognition on resource-constrained devices.
  • The model facilitates the deployment of deep learning in security applications.
  • FN13 provides a viable alternative to computationally intensive face recognition systems.