Towards Large-Scale 3D Face Recognition

3D face recognition holds great promise in achieving robustness to pose, expressions and occlusions. However, 3D face recognition algorithms are still far behind their 2D counterparts due to the lack of large-scale datasets. We present a model based algorithm for 3D face recognition and test its performance by combining two large public datasets of 3D faces. We propose a Fully Convolutional Deep Network (FCDN) to initialize our algorithm. Reliable seed points are then extracted from each 3D face by evolving level set curves with a single curvature dependent adaptive speed function. We then establish dense correspondence between the faces in the training set by matching the surface around the seed points on a template face to the ones on the target faces. A morphable model is then fitted to probe faces and face recognition is performed by matching the parameters of the probe and gallery faces. Our algorithm achieves state of the art landmark localization results. Face recognition results on the combined FRGCv2 and Bosphorus datasets show that our method is effective in recognizing query faces with real world variations in pose and expression, and with occlusion and missing data despite a huge gallery. Comparing results of individual and combined datasets show that the recognition accuracy drops when the size of the gallery increases.