| Abstract | dc.description.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. | en_US |
