# Does Generative Face Completion Help Face Recognition?

arXiv: https://arxiv.org/abs/1906.02858 · HTML: https://mlsanity.com/paper/1906.02858/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2019.

## Built on by (most cited first, 9 of 9)

- [Survey on Deep Face Restoration: From Non-blind to Blind and Beyond](https://mlsanity.com/paper/2309.15490/) (2023)
- [Pixel-Level Face Image Quality Assessment for Explainable Face Recognition](https://mlsanity.com/paper/2110.11001/) (2021)
- [Biometrics in the Era of COVID-19: Challenges and Opportunities](https://mlsanity.com/paper/2102.09258/) (2021)
- [Deep Face Video Inpainting via UV Mapping](https://mlsanity.com/paper/2109.00681/) (2021)
- [Fun Selfie Filters in Face Recognition: Impact Assessment and Removal](https://mlsanity.com/paper/2202.06022/) (2022)
- [Let's Get the FACS Straight -- Reconstructing Obstructed Facial Features](https://mlsanity.com/paper/2311.05221/) (2023)
- [Face Beneath the Ink: Synthetic Data and Tattoo Removal with Application to Face Recognition](https://mlsanity.com/paper/2202.05297/) (2022)
- [Look Through Masks: Towards Masked Face Recognition with De-Occlusion Distillation](https://mlsanity.com/paper/2409.12385/) (2024)
- [State of the Art: Face Recognition](https://mlsanity.com/paper/2108.11821/) (2021)

## Abstract (arXiv)

Face occlusions, covering either the majority or discriminative parts of the face, can break facial perception and produce a drastic loss of information. Biometric systems such as recent deep face recognition models are not immune to obstructions or other objects covering parts of the face. While most of the current face recognition methods are not optimized to handle occlusions, there have been a few attempts to improve robustness directly in the training stage. Unlike those, we propose to study the effect of generative face completion on the recognition. We offer a face completion encoder-decoder, based on a convolutional operator with a gating mechanism, trained with an ample set of face occlusions. To systematically evaluate the impact of realistic occlusions on recognition, we propose to play the occlusion game: we render 3D objects onto different face parts, providing precious knowledge of what the impact is of effectively removing those occlusions. Extensive experiments on the Labeled Faces in the Wild (LFW), and its more difficult variant LFW-BLUFR, testify that face completion is able to partially restore face perception in machine vision systems for improved recognition.