Robust Face Recognition using Local Illumination Normalization and Discriminant Feature Point Selection

TL;DR

This paper introduces a face recognition method that uses Gabor wavelet features with local illumination normalization to improve robustness against lighting variations, enhancing practical application reliability.

Contribution

It proposes a novel Gabor wavelet feature extraction technique that maintains stability under local illumination changes, addressing a key challenge in face recognition.

Findings

Improved recognition accuracy under varying illumination conditions.

Demonstrated effectiveness through experimental results.

Enhanced robustness compared to traditional methods.

Abstract

Face recognition systems must be robust to the variation of various factors such as facial expression, illumination, head pose and aging. Especially, the robustness against illumination variation is one of the most important problems to be solved for the practical use of face recognition systems. Gabor wavelet is widely used in face detection and recognition because it gives the possibility to simulate the function of human visual system. In this paper, we propose a method for extracting Gabor wavelet features which is stable under the variation of local illumination and show experiment results demonstrating its effectiveness.