Performance of LDA Versus PCA in Nonideal Lighting Environment

Abstract

Automatic face recognition (AFR) is used to identify people by processing their photos or snapshots of faces, either in off-line or real-time manners, respectively. However, classical face recognition techniques have been reported to suffer from substantial degradation in performance when person image is subjected to nonideal lighting or some types of occlusion. In real life we may well encounter a certain type of nonideal lighting such as side-shadowing of the face, where substantial part of the face can be totally occluded or masked. In this work, we examine and evaluate the performance of two famous statistical approaches for AFR namely PCA and LDA in terms of face recognition rate (FRR), when both are operating on particular ill-illuminated image exemplified by side-shadowing occlusion with addition of “salt-and-pepper” noise, which is often the encountered case. The two suggested AFR techniques are the well-reputed principal component analysis (PCA) and linear discriminate analysis (LDA). A computer simulation has been executed testing both PCA and LDA and the simulation outcomes indicate much better performance of LDA over PCA in terms of FRR for this particular type of image occlusion.