Three-dimensional Deformation Model And Pca-based Face Recognition Research

Face recognition is one of the most popular research fields at present.Recently,most of the researches on face recognition are based on 2D face images. Because of the influence of illumination,pose variation and expression,the improvement of recognition accuracy of 2D face recognition is greatly impeded. This makes it still difficult to build a robust face recognition system. 3D model holds more rich information than 2D image,so implementing face recognition on 3D face model is one of the effective approaches to tackle the present problems.This paper presents a method for face recognition across variations in pose,ranging from frontal to profile views.Firstly,at the beginning,this paper introduces the development of face recognition technology and several popular face recognition methods.Secondly,as the 3D face databases are more important and significant for further research on faces,this paper introduces some 3D face databases which have been constructed. This paper researchs on the key technology in construction of a 3D face database. By acquiring the human faces through the CyberWare 3D scanner ,a 3D color face database is constructed. To build 3D face database ,the original 3D data should be preprocessed ,and the 3D faces should be standardized in a uniform format. The key to the problem of standardization is the pixel-to-pixel alignment s among the 3D faces,which is a difficult problem in computer graphics and computer vision.Lastly,this paper describes the construction of the morphable model,an algorithm to fit the model to images,and a framework for face identification. Starting from an example set of 3D face models,we derive a morphable face model by transforming the shape and texture of the examples into a vector space representation. This means that the model is learned from a set of textured 3D scan model of human heads. New faces and expressions can be modeled by forming linear combinations of the prototypes. Shape and texture constraints derived from the statistics of our 3D face models are used to guide manual modeling or automated matching algorithms. The algorithm simulates the process of image formation in 3D space,using computer graphics,and it estimates 3D shape and texture of faces from single images. The estimate is achieved by fitting a statistical,morphable model of 3D faces to images. By rendering the recovered 3D face in various poses,we get several images of one person. These images constitute a training image database. The face recognition system presented in this paper uses the training image database. The system uses the PCA-based method to recognize the faces. To verify the effectiveness of the proposed approach,we make two different experiments. We analyse the results and compare them. The experimental results on the training image database of frontal and profile views shows that the recognition rate of the proposed method is higher than those of directly using the morphable method.