Face recognition under illumination is really challenging. This paper proposes an effective method to produce illumination-invariant features for images with various levels of illumination. The proposed method seamlessly combines adaptive homomorphic filtering, simplified logarithmic fractal dimension, and complete eight local directional patterns to produce illumination-invariant representations. Our extensive experiments show that the proposed method outperforms two of its variant methods and nine state-of-the-art methods, and achieves the overall face recognition accuracy of 99.47%, 94.55%, 99.53%, and 86.63% on Yale B, extended Yale B, CMU-PIE, and AR face databases, respectively, when using one image per subject for training. It also outperforms the compared methods on the Honda UCSD video database using five images per subject for training and considering all necessary steps including face detection, landmark localization, face normalization, and face matching to recognize faces. Our evaluations using receiver operating characteristic (ROC) curves also verify the proposed method has the best verification and discrimination ability compared with other peer methods.
['Mohammad Reza Faraji', 'Xiaojun Qi']
Face recognition under varying illuminations using logarithmic fractal dimension-based complete eight local directional patterns