Facial Feature Detection Using Multiresolution Decomposition and Hillcrest-Valley Classification with Adaptive Mean Filter

Automatic facial feature detection is one of the most important topics in computer vision and there are still many open problems that have not been solved. Nonuniform illumination is among one of those problems. This paper proposes a novel method for solving nonuniform illumination problem using multiresolution decomposition and a new technique called hillcreast-valley classification with adaptive mean filter to normalize illumination and detect dominant facial features, such as eyes, nose and mouth automatically. The proposed method is divided into three modules: eye detection, nose detection, and mouth detection modules. In this method, a single face image is divided into three regions: eye, nose, and mouth regions, then we use multiresolution decomposition to detect the eyes, and use thresholding to detect the nose and the mouth. For multiresolution decomposition, we decompose the eye region into small blocks and use hillcrest-valley classification with adaptive mean filter to classify each block as either a high or low-intensity region. Each low-intensity(valley) region is then decomposed into smaller blocks and each block is classified as either high- or low-intersity region. The low-intensity regions are then defined as the eyes. Finally the nose and the mouth are detected using thresholding. The method was evaluated on the YaleB face database that consists of face images taken by different illumination variations and the experimental results indicate that our proposed method achieves high accuracy rate.