Enhancing Face Normalization Based on Novel Normal Facial Diagram
5 Pages Posted: 28 Jan 2014
Date Written: September 26, 2013
Abstract
In this paper, an improved version of my previous work is introduced, i.e. an enhanced and an efficient method of emotion normalization for the faces recognition is proposed. This approach is also used to reduce the computational overhead by 1/n (n is number of images for an individual in a training set) and minimize distance between source image and target images. This method is mainly based on deforming a source face to represent corresponding normal face using proposed Normal Face Diagram (NFD) in order to normalize the source face. An imaginary NFD based on the location of the pair of eyes of the source face is drawn, the diagram consists of edges connecting vertices of the pair of eye brows, eyes, nose tip, end points of the lips and mid of the bottom lip. So source facial diagram (SFD) is drawn based on extracted feature points. The genetic algorithm is used to search for possible face region, while the Eigenface technique is used to determine the fitness of the region. After alignment of an eye point (in our method left eye is aligned) determine the distance between those features of SFD and NFD and store Euclidean distance between these graph nodes. Then project the vertex of the SFD along with the corresponding vertex of the NFD by adjustment of the magnitude and direction of these motion vectors.
Keywords: Genetic Algorithms, Eigenface, Motion vectors, Normal facial diagram, Euclidean distance, Motion vector
Suggested Citation: Suggested Citation