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.
Abstract (translated)
面部阻塞，覆盖了面部的大部分或有辨别力的部分，会破坏面部感知，造成信息的严重丢失。生物识别系统，如最近的深面部识别模型，并不能免疫障碍物或其他物体覆盖面部的一部分。虽然目前大多数的人脸识别方法都没有经过优化来处理阻塞，但在训练阶段，有一些尝试可以直接提高鲁棒性。与此不同的是，我们建议研究生成人脸完成对识别的影响。我们提供了一种基于卷积算符和选通机制的人脸完成编码器解码器，经过了一系列人脸封闭训练。为了系统地评估现实遮挡对识别的影响，我们建议玩遮挡游戏：我们将3D对象渲染到不同的面部部位，提供有效消除这些遮挡的影响的宝贵知识。对野生标记人脸（LFW）及其更困难的变体LFW-BLUFR进行了大量的实验，证明人脸完成可以部分恢复机器视觉系统中的人脸感知，从而提高识别能力。
URL
https://arxiv.org/abs/1906.02858
