Face Recognition With Variant Illumination And Poses

Automatic face recognition has great potential applications in public security, intelligent surveillance, digital personal identify, electronic commerce, multimedia, digital entertainment, etc. and has great theory value in many subjects, so face recognition has attracted much research attention from the research institutes, governments, military and security departments. Over the past 30 years, great progress and developments have been made in face recognition. Now, under the controlled and cooperative conditions, face recognition systems perform very well, but under the uncontrolled and uncooperative conditions, especially when the illumination in face images and facial poses are variant, the recognition rate degrade quickly and face recognition is also a great challenge.In this paper, the effect of illumination in face recognition is addressed. On the analysis of the present methods for this problem, a low dimensional illumination space representation (LDISR) of human faces for arbitrary lighting conditions is proposed in this paper. The LDISR is based on the observation that 9 basis point light resources can represent almost arbitrary lighting conditions for face recognition application and different human faces have the similar LDISR. The principal component analysis (PCA) and nearest neighbor clustering method are adopted to obtain the 9 basis point lights. The 9 basis images under the 9 basis point light sources construct a LDISR which can represent almost all face images under arbitrary lighting conditions. Illumination ratio image (IRI) is adopted to generate virtual face images under different illuminations. Based on the virtual face images, face recognition with variant illumination can be performed. The advantage of LDISR is that it can not only synthesize a virtual face image when given lighting condition but also estimate lighting conditions when given a face image, and it can be trained on images of one human face and can be used for all human faces. The LDISR is proposed for human faces, but it can be expanded for other objects.This paper proposes a new method of modeling 3D face based on single frontal face image. Using the 3D face model, virtual face images under different poses can be generated and then be used as template images for face recognition with variant poses. Recovering the 3D shape from 2D images is the basic problem in computer vision. The traditional methods of recovering 3D face model from 2D face images need multi images, image sequence, stereo images, or front and profile images, and the qualifications are difficult to fulfill in many practical applications. The proposed method needs only one frontal face image, facilitates the use in practical applications, and has great potential applications. As the experiment shows, face model synthesized by our method can fit for the applications such as face recognition, expression animation and human computer interaction.In order to estimate the facial 3D pose quickly and accurately, Area Model and...