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ABSTRACT: In recent years, 3D face recognition has become a popular solution to deal with the problem of pose-invariant face recognition. The majority of 3D face data are, however, actually 2.5D which are sensitive to pose variations. This paper presents a novel Geodesic Texture Warping (GTW) solution for 2.5D pose-invariant face recognition. In this method, we use the geodesic distance computed on a 2.5D face scan to warp the texture of a rotated face to that of a frontal one to perform matching. A feasibility and effectiveness investigation for the proposed method is conducted using a wide range of experiments including samples with different face rotations. The encouraging experimental results demonstrate that the proposed method achieves much higher accuracy than the state-of-the-art method with a low computational cost.
Control Automation Robotics & Vision (ICARCV), 2010 11th International Conference on; 01/2011
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ABSTRACT: Numerous methods have been proposed for the expression-invariant 3D face recognition, but a little attention is given to the local-based representation for the texture of the 3D images. In this paper, we propose an expression-invariant 3D face recognition approach based on the locally extracted moments of the texture when only one exemplar per person is available. We use a geodesic texture transform accompanied by Pseudo Zernike Moments to extract local feature vectors from the texture of a face. An extensive experimental investigation is conducted using publicly available BU-3DFE face databases covering face recognition under expression variations. The performance of the proposed method is compared with the performance of two benchmark approaches. The encouraging experimental results demonstrate that the proposed method can be used for 3D face recognition in single model databases.
Digital Image Computing: Techniques and Applications (DICTA), 2010 International Conference on; 01/2011
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ABSTRACT: In this paper, a new method for face localization in color images, which is based on co-evolutionary systems, is introduced. The proposed method uses a co-evolutionary system to locate the eyes in a face image. The used coevolutionary system involves two genetic algorithm models. The first GA model searches for a solution in the given environment, and the second GA model searches for useful genetic information in the first GA model. In the next step, by using the location of eyes in image the parameters of face's bounding ellipse (center, orientation, major and minor axis) are computed. To evaluate and compare the proposed method with other methods, high order Pseudo Zernike Moments (PZM) are utilized to produce feature vectors and a Radial Basis Function (RBF) neural network is used as the classifier. Simulation results indicate that the speed and accuracy of the new system using the proposed face localization method which uses a co-evolutionary approach is higher than the system proposed in.
Digital Image Computing: Techniques and Applications (DICTA), 2010 International Conference on; 01/2011
