# Does Generative Face Completion Help Face Recognition?

## TL;DR

This paper investigates whether generative face completion can enhance face recognition accuracy under occlusions by restoring occluded facial regions, demonstrating partial success in improving recognition performance.

## Contribution

It introduces a face completion encoder-decoder trained on occluded faces and systematically evaluates its impact on recognition with realistic occlusion simulations.

## Key findings

- Face completion improves recognition accuracy on occluded faces.
- The method performs well on LFW and LFW-BLUFR datasets.
- Occlusion removal via face completion partially restores face perception.

## Abstract

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.