Illumination compensation and normalization in eigenspace-based face recognition: A comparative study of different pre-processing approaches

Abstract

The aim of this work is to investigate illumination compensation and normalization in eigenspace-based face recognition by carrying out an independent comparative study among several pre-processing algorithms. This research is motivated by the lack of direct and detailed comparisons of those algorithms in equal working conditions. The results of this comparative study intend to be a guide for the developers of face recognitions systems. The study focuses on algorithms with the following properties: (i) general purpose, (ii) no modeling steps or training images required, (iii) simplicity, (iv) high speed, and (v) high performance in terms of recognition rates. Thus, herein five different algorithms are compared, by using them as a pre-processing stage in 16 different eigenspace-based face recognition systems. The comparative study is carried out in a face identification scenario using a large amount of images from the PIE, Yale B and Notre Dame face databases. As a result of this study we concluded that the most suitable algorithms for achieving illumination compensation and normalization in eigenspace-based face recognition are SQI and the modified LBP transform.