Toward Robust Facial Authentication for Low-Power Edge-AI Consumer Devices

Robust authentication for low-power consumer devices without a keyboard remains a challenge. The recent availability of low-power neural accelerator hardware, combined with improvements in neural facial recognition algorithms provides enabling technology for low-power, on-device facial authentication. The present research work explores a number of approaches to test the robustness of a state-of-the-art facial recognition (FR) technique, Arcface for such end-to-end applications. As extreme lighting conditions and facial pose are the two more challenging scenarios for FR we focus on these. Due to the general lack of large-scale multiple-identity datasets, GAN-based re-lighting and pose techniques are used to explore the effects on FR performance. These results are further validated on the best available multi-identity datasets - MultiPIE and BIWI. The results show that FR is quite robust to pose variations up to 45–55 degrees, but the outcomes are not definitive for the tested lighting scenarios. For lighting, the tested GAN-based relighting augmentations show significant effects on FR robustness. However, the lighting scenarios from MultiPIE dataset - the best available public dataset - show some conflicting results. It is unclear if this is due to an incorrectly learned GAN relighting transformation or, alternatively, to mixed ambient/directional lighting scenes in the dataset. However, it is shown that the GAN-induced FR errors for extreme lighting conditions can be corrected by fine-tuning the FR network layers. The conclusions support the feasibility of implementing a robust authentication method for low-power consumer devices.