3D Landmark-based Face Detection and Recognition System for Large Poses

Most face recognition algorithms achieve great per-formances in small poses, but they are unable to extract intact features for large-pose faces. In order to improve large-pose face recognition, we propose a 3D landmark-based face recognition system. We first extract RGB features from a face recognition model. Then we predict 3D landmarks and facial pose degrees via a projected 3DMM vector in the 3D landmark model. To test images, we compute the distance of two RGB features and rotate two 3D 68-point landmarks to the frontal view. If both of two features are smaller than a threshold, we purport these two images are from the same person. Compared to traditional face recognition CNN methods, the proposed method not only consider RGB features but 3D estimated landmarks. With this information, we can achieve higher performance. We conduct experiments on large-pose face datasets, CPLFW, CFPFP and IJB-B. The results outperform the state-of-the-art methods. We achieve recognition rate of 94.15% on CPLFW, which is 1.02% higher than the Curricular Face [1], and 98.97% on CFPFP, which is 0.6% higher than Curricular Face [1]. Compared to Curricular Face [1], our model reduces 13M parameters usage and achieves 94.9 % on IJB- B.