Face recognition has received much interest in the last decade, as the need for reliable personal identification security has become ever more critical. At present for face recognition to be a viable personal identification method an accurate low cost solution is required. Many two-dimensional (2D) face recognition systems have been implemented, which demonstrate the potential of using face rec...

Face recognition based on the analysis of 3D scans has been an active research subject over the last few years. However, the impact of the resolution of 3D scans on the recognition process has not been addressed explicitly yet being of primal importance after the introduction of a new generation of low cost 4D scanning devices. These devices are capable of combined depth/rgb acquisition over ti...

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...

Most of the face recognition works focus on specific modules or demonstrate a research idea. This paper presents a pose-invariant 3D-aided 2D face recognition system (UR2D) that is robust to pose variations as large as 90◦ by leveraging deep learning technology. The architecture and the interface of UR2D are described, and each module is introduced in detail. Extensive experiments are conducted...

In this paper, we present a novel approach for fusing 3D Local Binary Pattrns (3DLBP) and singular Value Decomposition (SVD) for face recognition when ”Kinect” is used as the 3D face scanner. Another approach is used to compare the 3DLBP method, fused with SVD, with other methods proposed in the literature for face recognition by using Kinect. Experimental results on FRGC 2.0 face dataset showe...

In this paper, a novel real-time 3D and color sensor for the mid-distance range (0.1-3m) based on color-encoded structured light is presented. The sensor is integrated using lowcost of-the-shelf components and allows the combination of 2D and 3D image processing algorithms, since it provides a 2D color image of the scene in addition to the range data. Its design is focused on enabling the syste...

2D intensity images and 3D shape models are both useful for face recognition, but in different ways. While algorithms have long been developed using 2D or 3D data, recently has seen work on combining both into multi-modal face biometrics to achieve higher performance. However, the fusion of the two modalities has mostly been at the decision level, based on scores obtained from independent 2D an...

This paper proposes an encoder-decoder network to disentangle shape features during 3D face reconstruction from single 2D images, such that the tasks of reconstructing accurate 3D face shapes and learning discriminative shape features for face recognition can be accomplished simultaneously. Unlike existing 3D face reconstruction methods, our proposed method directly regresses dense 3D face shap...

In traditional 2D and 3D face recognition systems, different features are extracted from 2D and 3D face images, and then are fused to improve the recognition performance. The shortage of these methods is that they neglect the intrinsic complementary features between 2D and 3D data. In this paper, we investigate the possibility of extracting and scalable fusing common features from 2D intensity ...

Model-based face analysis is a general paradigm with applications that include face recognition, expression recognition, lipreading, head pose estimation, and gaze estimation. A face model is first constructed from a collection of training data, either 2D images or 3D range scans. The face model is then fit to the input image(s) and the model parameters used in whatever the application is. Most...