A Robust Approach for Eye Localization Under Variable Illuminations

Illumination variation is a main obstacle in facial feature detection. This paper presents a novel automated approach that localizes eyes in gray-scale face images and that is robust to illumination changes. The approach does not require prior knowledge about face orientation and illumination strength. Other advantages are that no initialization and training process are needed. Based on an edge map obtained via multi-resolution wavelet transform, this approach first segments an image into different inhomogeneously illuminated regions. The illumination of every region is then adjusted so that the features´ details are more pronounced. To locate the different facial features, for every region, Gabor-based image is constructed from the re-lit image. The eyes sub-regions are then identified using the edge map of the re-lit image. This method has been applied successfully to the images of the Yale B face database that have different illuminations.