探索神经网络在人脸图像重建识别系统中的应用

Evgeny Igorevich Markin, V. Zuparova, A. Martyshkin
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引用次数: 0

摘要

利用计算机视觉在数字图像中识别人是该领域的一个关键方面。外部物体的存在,例如覆盖部分面部的医用口罩,会大大降低识别精度,并将误差从5%增加到50%,具体取决于算法。本文研究了使用神经网络,特别是生成对抗网络(GAN)来解决重建被医用口罩覆盖的人脸图像的问题,以提高人脸识别的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring the application of neural networks for facial image reconstruction in recognition systems
Identifying a person in a digital image using computer vision is a crucial aspect of this field. The presence of external objects, such as medical masks that cover part of the face, can drastically reduce recognition accuracy and increase errors from 5% to 50%, depending on the algorithm. This paper investigates the use of neural networks, in particular the generative adversarial network (GAN), to solve the problem of reconstructing an image of a face covered by a medical mask to improve face recognition accuracy.
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