Automatic Human Faces Morphing Using Genetic Algorithms Based Control Points Selection

In this paper, a genetic algorithm guided control point extraction method that enables automatic face image morphing is proposed. This method can morph two face images by automatically detecting all the control points necessary to perform the morph. A face detection neural network, edge detection and medium filters are employed to detect the face position and features. Five control points, for both the source and target images, are then extracted based on the facial features. A triangulation method is then used to match and warp the source to the target image using the control points. Finally, color interpolation is done using a color Gaussian model that calculates the color for each particular frame depending on the number of frames used. A real coded Genetic Algorithm (GA) is used to aid the facial features extraction by overcoming size and orientation changes. We achieve very high quality morphs at high speed.