Differential 3D Facial Recognition: Adding 3D to Your State-of-the-Art 2D Method
This work addresses security and reliability issues in face recognition systems for applications like authentication, though it is incremental as it builds on existing 2D methods.
The paper tackles the problem of enhancing 2D face recognition with 3D features to improve robustness against spoofing attacks and low-light conditions, achieving significant performance boosts on the ND-2006 dataset.
Active illumination is a prominent complement to enhance 2D face recognition and make it more robust, e.g., to spoofing attacks and low-light conditions. In the present work we show that it is possible to adopt active illumination to enhance state-of-the-art 2D face recognition approaches with 3D features, while bypassing the complicated task of 3D reconstruction. The key idea is to project over the test face a high spatial frequency pattern, which allows us to simultaneously recover real 3D information plus a standard 2D facial image. Therefore, state-of-the-art 2D face recognition solution can be transparently applied, while from the high frequency component of the input image, complementary 3D facial features are extracted. Experimental results on ND-2006 dataset show that the proposed ideas can significantly boost face recognition performance and dramatically improve the robustness to spoofing attacks.