Face recognition using PCA integrated with Delaunay triangulation

TL;DR

This paper proposes a face recognition algorithm that combines PCA and Delaunay triangulation, aiming to improve accuracy by leveraging facial landmarks and eigenfaces.

Contribution

It introduces a novel integration of PCA with Delaunay triangulation for enhanced face recognition performance.

Findings

Improved recognition accuracy over traditional PCA.

Effective use of facial landmarks in triangulation.

Comparison showing advantages of the combined method.

Abstract

Face Recognition is most used for biometric user authentication that identifies a user based on his or her facial features. The system is in high demand, as it is used by many businesses and employed in many devices such as smartphones and surveillance cameras. However, one frequent problem that is still observed in this user-verification method is its accuracy rate. Numerous approaches and algorithms have been experimented to improve the stated flaw of the system. This research develops one such algorithm that utilizes a combination of two different approaches. Using the concepts from Linear Algebra and computational geometry, the research examines the integration of Principal Component Analysis with Delaunay Triangulation; the method triangulates a set of face landmark points and obtains eigenfaces of the provided images. It compares the algorithm with traditional PCA and discusses the inclusion of different face landmark points to deliver an effective recognition rate.