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SELM: Siamese extreme learning machine with application to face biometrics.

Wasu Kudisthalert1, Kitsuchart Pasupa1, Aythami Morales2

  • 1Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok, 10520 Thailand.

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Summary

Siamese Extreme Learning Machine (SELM) enhances face verification by processing two images simultaneously. This novel approach improves accuracy and computational efficiency compared to traditional methods.

Keywords:
Extreme learning machineFace recognitionFeature embeddingSiamese network

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Extreme Learning Machine (ELM) is a fast classification method but struggles with face verification due to its single-input structure.
  • Existing ELM adaptations for face verification use concatenated inputs, doubling computational cost and hindering metric learning.

Purpose of the Study:

  • To introduce a Siamese Extreme Learning Machine (SELM) designed for parallel processing of dual-input data streams.
  • To develop a Gender-Ethnicity-dependent triplet feature for improved facial feature extraction across demographic groups.

Main Methods:

  • Proposed SELM architecture with a dual-stream Siamese network for parallel data processing.
  • Introduced a novel Gender-Ethnicity-dependent triplet feature for specialized demographic group analysis.
  • Conducted comparative experiments against ELM and Deep Convolutional Neural Network (DCNN).

Main Results:

  • The proposed feature achieved accuracy and AUC.
  • SELM combined with the proposed feature yielded accuracy and AUC.
  • SELM demonstrated superior performance over DCNN and standard ELM methods.

Conclusions:

  • SELM effectively addresses the limitations of ELM in face verification tasks.
  • The Gender-Ethnicity-dependent triplet feature significantly enhances facial recognition accuracy.
  • SELM offers a more efficient and accurate solution for face verification compared to existing methods.