An Enhanced Histogram Matching Approach Using the Retinal Filter's Compression Function for Illumination Normalization in Face Recognition

TLDR: A new illumination normalization approach based on enhancing the image resulting from the HM using the gamma correction and the Retinal filter's compression function, which proves its flexibility to different face recognition methods and the suitability for real-life systems in which perfect aligning of the face is not a simple task.

Abstract: Although many face recognition techniques have been proposed, recent evaluations in FRVT2006 conclude that relaxing the illumination condition has a dramatic effect on their recognition performance. Among many illumination normalization approaches, histogram matching (HM) is considered one of the most common image-processing-based approaches to cope with illumination. This paper introduces a new illumination normalization approach based on enhancing the image resulting from the HM using the gamma correction and the Retinal filter's compression function; we call it GAMMA-HM-COMP approach. Rather than many other approaches, the proposed one proves its flexibility to different face recognition methods and the suitability for real-life systems in which perfect aligning of the face is not a simple task. The efficiency of the proposed approach is empirically demonstrated using both a PCA-based (Eigenface) and a frequency-based (Spectroface) face recognition methods on both aligned and non-aligned versions of Yale B database. It leads to average increasing in recognition rates ranges from 4 ~ 7 % over HM alone.