3-D face detection, landmark localization, and registration using a point distribution model
IEEE Transactions on Multimedia
Fast Approximation of the Discrete Gauss Transform in Higher Dimensions
Journal of Scientific Computing
| Hi-index | 0.01 |
In view of today驴s security concerns, 3D face reconstruction and recognition has gained a significant position in computer vision research. Depth information of a 3D face can be used to solve the problems of illumination and pose variation associated with face recognition. Registration is an integral part of any reconstruction process and hence we focus on the problem of automatic registration of 3D face point sets through a criterion based on Gaussian fields. The method defines a simple energy function, which is always differentiable and convex in a large neighborhood of the alignment parameters; allowing for the use of powerful standard optimization techniques. The new method overcomes the necessity of close initialization, which is required by Iterative Closest Point algorithm. Moreover, the use of the Fast Gauss Transform reduces the computational complexity of the registration algorithm.