An experimental study for the effects of noise on face recognition algorithms under varying illumination

When the illumination changes, the appearance of facial images will change dramatically. Lighting changes make face recognition a very challenging and difficult job. In addition, the effects of noise on existing face recognition methods have been neglected in the literature, to the best of our knowledge. In this work, we study the effects of noise on existing illumination-invariant face recognition methods. We tested such noise as Gaussian white noise, Poisson noise, salt & pepper noise, speckle noise, etc. In total, 21 methods have been included in this study in this work. We find out that, when noise is added to facial images, Tan and Triggs' method achieves the best results for both the extended Yale B face database and the CMU-PIE face database. When facial images do not contain noise, isotropic smoothing is preferred because it obtains the highest average recognition rate (96%) for the extended Yale B face database and 16 methods obtain perfect correct recognition rates (100%) for the CMU-PIE face database.