Hypergraph Laplacian Eigenmaps and Face Recognition Problems

TL;DR

This paper introduces a novel hypergraph Laplacian Eigenmaps method combined with classification techniques to improve face recognition accuracy, demonstrating comparable results to existing methods.

Contribution

The paper proposes a new hypergraph Laplacian Eigenmaps approach and explores its integration with k-NN and kernel ridge regression for face recognition.

Findings

Accuracy similar to existing symmetric normalized hypergraph Laplacian Eigenmaps

Effective combination with k-NN and kernel ridge regression

Potential for improved face recognition performance

Abstract

Face recognition is a very important topic in data science and biometric security research areas. It has multiple applications in military, finance, and retail, to name a few. In this paper, the novel hypergraph Laplacian Eigenmaps will be proposed and combine with the k nearest-neighbor method and/or with the kernel ridge regression method to solve the face recognition problem. Experimental results illustrate that the accuracy of the combination of the novel hypergraph Laplacian Eigenmaps and one specific classification system is similar to the accuracy of the combination of the old symmetric normalized hypergraph Laplacian Eigenmaps method and one specific classification system.