A comparative study for the effects of noise on illumination invariant face recognition algorithms

Abstract

In the literature, the effects of noise on existing face recognition algorithms are neglected, to the best of our knowledge. In this paper, for the first time, we perform an experimental study for the effects of noise on existing illumination-invariant face recognition algorithms. In total, twenty-one algorithms have been included in this study in this paper. We find that, when noise is present in face images, Tan and Triggs’ method achieves the highest correct recognition rates for both the extended Yale B face database and the CMU-PIE face database. If face images do not contain noise, isotropic smoothing is preferred because this method obtains the highest average recognition rate (96 %) for the extended Yale B database and 16 out of 21 methods achieve 100 % correct recognition rates for the CMU-PIE face database.