  The recent availability of low-power neural accelerator hardware, combined
with improvements in end-to-end neural facial recognition algorithms provides,
enabling technology for on-device facial authentication. The present research
work examines the effects of directional lighting on a State-of-Art(SoA) neural
face recognizer. A synthetic re-lighting technique is used to augment data
samples due to the lack of public data-sets with sufficient directional
lighting variations. Top lighting and its variants (top-left, top-right) are
found to have minimal effect on accuracy, while bottom-left or bottom-right
directional lighting has the most pronounced effects. Following the fine-tuning
of network weights, the face recognition model is shown to achieve close to the
original Receiver Operating Characteristic curve (ROC)performance across all
lighting conditions and demonstrates an ability to generalize beyond the
lighting augmentations used in the fine-tuning data-set. This work shows that
an SoA neural face recognition model can be tuned to compensate for directional
lighting effects, removing the need for a pre-processing step before applying
facial recognition.
