Published October 21, 2025 | Version v1
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Beyond Pixels: The Mathematical Legacy of Classical Face Recognition

Authors/Creators

  • 1. ROR icon Tribhuvan University

Description

This comprehensive review article examines the mathematical foundations and practical implementations of classical face recognition methods that dominated computer vision from 1987 to 2014. The paper provides rigorous mathematical derivations for three foundational approaches: Eigenfaces (Principal Component Analysis), Fisherfaces (Linear Discriminant Analysis), and Local Binary Patterns. Through detailed algorithmic explanations, comparative performance analyses across benchmark datasets, and connections to modern deep learning, this work preserves the theoretical insights of traditional methods while contextualizing their historical significance. Despite achieving 90-95% accuracy in controlled settings, lower than contemporary deep learning's 99%+, these classical techniques maintain substantial pedagogical value and remain relevant for resource-constrained applications. The paper serves as both an educational resource for understanding fundamental computer vision concepts and a practical guide for scenarios requiring computational efficiency and interpretability.

 

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References

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