Abstract: Two related methods of fitting a three dimensional model, and a method of performing facial recognition, are disclosed. One method comprises estimating and refining geometric information using image landmarks on an object in a two dimensional image. The other method comprises estimating and refining photometric information of the object in the two dimensional image. Furthermore, a method of performing image recognition is provided.

Abstract: Two related methods of fitting a three dimensional model, and a method of performing facial recognition, are disclosed. One method comprises estimating and refining geometric information using image landmarks on an object in a two dimensional image. The other method comprises estimating and refining photometric information of the object in the two dimensional image. Furthermore, a method of performing image recognition is provided.

Abstract: A personal identity verification process and system adopts an error correcting output code (ECOC) classifier approach. Image data representing an input facial images is processed to generate a first (ECOC) classifier output vector and image data representing a class of different training images of the face of an individual are processed to generate a set of second ECOC classifier output vectors. Authenticity of the input facial image against the class of training images is evaluated by measuring distance between the first ECOC classifier output vector and the set of second ECOC classifier output vectors using a first order Minkowski metric.

Abstract: A personal identity verification process and system adopts an error correcting output code (ECOC) classifier approach. Image data representing an input facial images is processed to generate a first (ECOC) classifier output vector and image data representing a class of different training images of the face of an individual are processed to generate a set of second ECOC classifier output vectors. Authenticity of the input facial image against the class of training images is evaluated by measuring distance between the first ECOC classifier output vector and the set of second ECOC classifier output vectors using a first order Minkowski metric.

Abstract: A personal identity authentication process and system use a class specific linear discriminant transformation to test authenticity of a probe face image. A ‘client acceptance’ approach, an ‘imposter rejection’ approach and a ‘fused’ approach are described.

Abstract: A novel approach to 2D face authentication that is assisted by client specific 3D models is proposed. Each 3D model is acquired during the client enrolment together with the usual client template. Any 3D face model acquisition system may be used for the purpose. The future authentication of client's identity by the face biometric system is based on 2D probe only, with the stored 3D model and the client template used for reference. In a verification scenario, the authentication process is assisted by the 3D model associated to the claimed identity.

Abstract: A personal identity verification process and system adopts an error correcting output code (ECOC) classifier approach. Image data representing an input facial images is processed to generate a first (ECOC) classifier output vector and image data representing a class of different training images of the face of an individual are processed to generate a set of second ECOC classifier output vectors. Authenticity of the input facial image against the class of training images is evaluated by measuring distance between the first ECOC classifier output vector and the set of second ECOC classifier output vectors using a first order Minkowski metric.

Abstract: A personal identity authentication process and system use a class specific linear discriminant transformation to test authenticity of a probe face image. A ‘client acceptance’ approach, an ‘imposter rejection’ approach and a ‘fused’ approach are described.

Abstract: A Hough transform based method of estimating N parameters a=(a1, . . . , aN) of motion of a region Y in a first image to a following image, the first and following images represented, in a first spatial resolution, by intensities at pixels having coordinates in a coordinate system, the method including: determining the total support H(Y,a) as a sum of the values of an error function for the intensities at pixels in the region Y; determining the motion parameters a that give the total support a minimum value; the determining being made in steps of an iterative process moving along a series of parameter estimates a1, a2, . . .