Implementation of a New Methodology to Reduce the Effects of Changes of Illumination in Face Recognition-based Authentication

In this paper, the study of the effects of illumination changes in the process of face recognition is discussed. Additionally, a new methodology that integrates Single-Scale Retinex (SSR) and Scale Invariant Feature Transform (SIFT) is presented. The proposed methodology addresses the problem of illumination changes in face recognition-based authentication systems. To do this, the SSR transform eliminates the component of luminance from the face image and recovers the reflectance component which is invariant to illumination changes; then, the SIFT transform detects interest points from the reflectance component of the face image. The interest points are features rich in information and may be used to identify an individual from face images. The results have shown that the proposed methodology could reduce considerably the variability of the location of the features extracted from face images corresponding to the same scene but captured under different illumination conditions. Thus, it is obtained a solution to the problem of face recognition under illumination changes by extracting features robust enough to different illumination conditions. Finally, the results were obtained over a wide faces database affected by illumination changes.