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

Towards End-to-End Neural Face Authentication in the Wild -- Quantifying and Compensating for Directional Lighting Effects