Locating essential facial features using neural visual model

Facial feature detection plays an important role in applications such as human computer interaction, video surveillance, face detection and face recognition. We propose a facial feature detection algorithm for all types of face images in the presence of several image conditions. There are two main step: the facial feature extraction from original face image, and the coverage of the features by rectangular blocks. A neural visual model (NVM) is used to recognize all possibilities of facial feature positions for the first step. Input parameters are obtained from the face characteristics and the positions of facial features not including any intensity information. For the better results, some incorrect decisions of facial feature positions are improved by image processing technique called dilation. Our algorithm is successfully tested with various types of faces which are color images, gray images, binary images, wearing the sunglasses, wearing the scarf, lighting effect, noise and blurring images, color and sketch images from animated cartoon.