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揭开潜力:评估面具面部重建的图像绘制技术

Chandni Agarwal¹, Charul Bhatnagar¹

¹GLA University, Mathura, India.

Multimedia tools and applications

|June 26, 2023

概括

图像 inpainting 技术可以重建蒙面面孔,提高面部识别性能. 与其他方法相比,Gated Convolution模型在再生面罩面部图像方面表现出优异的结果.

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
图像处理图像处理

背景情况:

面部识别系统与蒙面面孔作斗争,显著降低了性能.
图像绘画,一种图像修复技术,显示了重建蒙面面孔的潜力.

研究的目的:

评估和比较三个图像绘制模型在再生蒙面面部的有效性.
评估图像绘制对提高面具个人面部识别的适用性.

主要方法:

三种图像染色模型的比较:PatchMatch,Edge Connect和自由形式的图像染色.
使用定制合成数据集 (蒙面脸-CelebA,蒙面脸-CelebA-HQ) 进行评估.
使用图像质量评估 (IQA) 指标和VGG16分类器评估性能.

主要成果:

封闭式卷积模型在重建蒙面面孔方面表现优于PatchMatch,Edge Connect和Free form图像 inpainting.
图像质量评估得分表明,Gated Convolution模型的性能优越.
VGG16分类器的结果证实了IQA的定量和定性发现.

结论:

图像 inpainting,特别是使用 Gated Convolution 模型,是一个有前途的方法来应对面具面孔的面部识别挑战.
这项研究验证了图像绘制在改善面部识别系统在真实世界场景中使用面罩的准确性方面的有效性.

关键词:

深度学习是一种深度学习.面部识别系统是面部识别系统.面部在绘画中的表情面部重建面部重建生成性的对抗网络.图像在绘画绘画面具面部识别系统面具面部识别系统

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