Face recognition under varying illumination based on adaptive homomorphic eight local directional patterns

Face recognition under varying illumination based on adaptive homomorphic eight local directional patterns This study proposes an illumination-invariant face-recognition method called adaptive homomorphic eight local directional pattern (AH-ELDP). AH-ELDP first uses adaptive homomorphic filtering to reduce the influence of illumination from an input face image. It then applies an interpolative enhancement function to stretch the filtered image. Finally, it produces eight directional edge images using Kirsch compass masks and uses all the directional information to create an illumination-insensitive representation. The author’s extensive experiments show that the AH-ELDP technique achieves the best face recognition accuracy of 99.45% for CMU-PIE face images, 96.67% for Yale B face images and 84.42% for Extended Yale B face images using one image per subject for training when compared to seven representative state-of-the-art techniques.