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

arXiv: https://arxiv.org/abs/2104.03854 · HTML: https://mlsanity.com/paper/2104.03854/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 49 references that are in the map.
- First posted on arXiv 8 Apr 2021.

## Builds on (most cited first, 12 of 12)

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [Ternary Weight Networks](https://mlsanity.com/paper/1605.04711/) (2016)
- [FaceScape: a Large-scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction](https://mlsanity.com/paper/2003.13989/) (2020)
- [A Survey of Methods for Low-Power Deep Learning and Computer Vision](https://mlsanity.com/paper/2003.11066/) (2020)
- [The Elements of End-to-end Deep Face Recognition: A Survey of Recent Advances](https://mlsanity.com/paper/2009.13290/) (2020)
- [A Comprehensive Analysis of Deep Learning Based Representation for Face Recognition](https://mlsanity.com/paper/1606.02894/) (2016)
- [Facial Expressions as a Vulnerability in Face Recognition](https://mlsanity.com/paper/2011.08809/) (2020)
- [Optimize Deep Convolutional Neural Network with Ternarized Weights and High Accuracy](https://mlsanity.com/paper/1807.07948/) (2018)
- [Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets](https://mlsanity.com/paper/1903.00389/) (2019)
- [Dataset Augmentation for Pose and Lighting Invariant Face Recognition](https://mlsanity.com/paper/1704.04326/) (2017)
- [Gender Effect on Face Recognition for a Large Longitudinal Database](https://mlsanity.com/paper/1811.03680/) (2018)

## Abstract (arXiv)

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.