In this paper, we present a method to generate novel realistic 3D face model using a model trained from real 3D face data. 3D face samples are an important data platform for model training, algorithm design. Subject to the constraint of acquisition technology, sizes of current 3D face databases are relatively small and insufficient. The presented method is used to solve this problem. First real...

Pose, illumination, expression and the generalization of such effects to unseen face data samples are the fundamental problems faced in face recognition. The significant contribution of this thesis is the ability to match any two face images with a large pose angle variation. This approach utilizes a proposed 3D prior face model in order to cover a wide range of poses. To achieve this, a rapid ...

To help visually impaired people recognize people in their daily life, a 3D face feature registration approach is proposed with a RGB-D sensor. Compared to 2D face recognition methods, 3D data based approaches are more robust to the influence of face orientations and illumination changes. Different from most 3D data based methods, we employ a one-step ICP registration approach that is much less...

In this paper, we present a novel 3D face modeling approach from a monocular video captured using a conventional camera. The proposed algorithm relies on matching a generic 3D face model to the outer contours of the face to be modeled and a few of its internal features. At the first stage of the method, we estimate the head pose by comparing the edges extracted from video frames, with the conto...

Face recognition is one of the most important abilities that the humans possess. There are several reasons for the growing interest in automated face recognition, including rising concerns for public security, the need for identity verification for physical and logical access to shared resources, and the need for face analysis and modeling techniques in multimedia data management and digital en...

This paper describes an automatic system for 3D face modeling using frontal and profile images taken by an ordinary digital camera. The system consists of four subsystems including frontal feature detection, profile feature detection, shape deformation, and texture generation modules. The frontal and profile feature detection modules automatically extract the facial parts such as the eye, nose,...

In this chapter we present two algorithms for 3D face modeling from a monocular video sequence. The first method is based on Structure from Motion (SfM), while the second one relies on contour adaptation over time. The SfM based method incorporates statistical measures of quality of the 3D estimate into the reconstruction algorithm. The initial multi-frame SfM estimate is smoothed using a gener...

In this article we present a low cost 3 0 face modeling scheme that uses only an overhead projectoq a camera and a plane object. We deal with the calibration of such a system, the 3 0 face reconstruction itself; and then the texturing of the resulting 3 0 shape.

Face Detection and Modeling for Recognition By Rein-Lien Hsu Face recognition has received substantial attention from researchers in biometrics, computer vision, pattern recognition, and cognitive psychology communities because of the increased attention being devoted to security, man-machine communication, content-based image retrieval, and image/video coding. We have proposed two automated re...

We present an algorithm for 3-D face modeling from a frontal and a profile view images of a person’s face. The algorithm starts by computing the 3D coordinates of automatically extracted facial feature points. The coordinates of the selected feature points are then used to deform a 3D generic face model to obtain a 3D face model for that person. Procrustes Analysis is used to globally minimize ...