Depth information has been proven useful for face recognition. However, existing depth-image-based face recognition methods still suffer from noisy depth values and varying poses and expressions. In this paper, we propose a novel method for normalizing facial depth images to frontal pose and neutral expression and extracting robust features from the normalized depth images. The method is implemented via two deep convolutional neural networks (DCNN), normalization network ( (Net_ N )) and feature extraction network ( (Net_ F )). Given a facial depth image, (Net_ N ) first converts it to an HHA image, from which the 3D face is reconstructed via a DCNN. (Net_ N ) then generates a pose-and-expression normalized (PEN) depth image from the reconstructed 3D face. The PEN depth image is finally passed to (Net_ F ), which extracts a robust feature representation via another DCNN for face recognition. Our preliminary evaluation results demonstrate the superiority of the proposed method in recognizing faces of arbitrary poses and expressions with depth images.
In this paper, we present a large-scale database consisting of low cost Kinect 3D face videos, namely Lock3DFace, for 3D face analysis, particularly for 3D Face Recognition (FR). To the best of our knowledge, Lock3DFace is currently the largest low cost 3D face database for public academic use. The 3D samples are highly noisy and contain a diversity of variations in expression, pose, occlusion, time lapse, and their corresponding texture and near infrared channels have changes in lighting condition and radiation intensity, allowing for evaluating FR methods in complex situations. Furthermore, based on Lock3DFace, we design the standard experimental protocol for low-cost 3D FR, and give the baseline performance of individual subsets belonging to different scenarios for fair comparison in the future. We present an algorithm that uses a low resolution 3D sensor for robust face recognition under challenging conditions. A preprocessing algorithm is proposed which exploits the facial symmetry at the 3D point cloud level to obtain a canonical frontal view, shape and texture, of the faces irrespective of their initial pose. This algorithm also fills holes and smooths the noisy depth data produced by the low resolution sensor. The canonical depth map and texture of a query face are then sparse approximated from separate dictionaries learned from training data. The texture is transformed from the RGB to Discriminant Color Space before sparse coding and the reconstruction errors from the two sparse coding steps are added for individual identities in the dictionary. The query face is assigned the identity with the smallest reconstruction error. Experiments are performed using a publicly available database containing over 5000 facial images (RGB-D) with varying poses, expressions, illumination and disguise, acquired using the Kinect sensor. Recognition rates are 96.7 for the RGB-D data and 88.7 for the noisy depth data alone. Our results justify the feasibility of low resolution 3D sensors for robust face recognition. Three-dimensional (3D) face models can intrinsically handle large pose face recognition problem. In this paper, we propose a novel pose-invariant face recognition method via RGB-D images. By employing depth, our method is able to handle self-occlusion and deformation, both of which are challenging problems in two-dimensional (2D) face recognition. Texture images in the gallery can be rendered to the same view as the probe via depth. Meanwhile, depth is also used for similarity measure via frontalization and symmetric filling. Finally, both texture and depth contribute to the final identity estimation. Experiments on Bosphorus, CurtinFaces, Eurecom, and Kiwi databases demonstrate that the additional depth information has improved the performance of face recognition with large pose variations and under even more challenging conditions. 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 an element of primal importance to enable the use of the new generation of consumer depth cameras for biometric purposes. In fact, these devices perform depth color acquisition over time at standard frame-rate, but with a low resolution compared to the 3D scanners typically used for acquiring 3D faces in recognition applications. Motivated by these considerations, in this paper, we define a super-resolution approach for 3D faces by which a sequence of low-resolution 3D face scans is processed to extract a higher resolution 3D face model. The proposed solution relies on the scaled iterative closest point procedure to align the low-resolution scans with each other, and estimates the value of the high-resolution 3D model through a 2D box-spline functions approximation. To evaluate the approach, we built—and made it publicly available—the Florence Superface dataset that collects high-resolution and low-resolution data for about 50 different persons. Qualitative and quantitative results are reported to demonstrate the accuracy of the proposed solution, also in comparison with alternative techniques.