基于恢复的隐蔽面部识别通过身份指导的Inpainting
Honglei Li1,2, Yifan Zhang1, Wenmin Wang1
1School of Computer Science and Engineering, Macau University of Science and Technology, Macau, China.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
隐蔽面部识别得到了ID-Inpainter的改进,这是一个新的身份指导面部 inpainting 模型. 这种方法有效地恢复封闭的面部区域,同时保持身份,提高识别准确性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 生物识别信息 生物识别信息
背景情况:
- 面部图像中的遮蔽对自动面部检测和识别系统来说是一个主要障碍.
- 现有的面部识别算法在被封闭的数据集上表现不佳,原因是关键身份信息丢失.
研究的目的:
- 开发一种先进的面部涂装技术,以保存身份信息,以改善隐蔽面部识别.
- 解决当前编码解码器在保留身份线索方面的局限性.
主要方法:
- 介绍ID-Inpainter,一个以身份为导向的生成对抗网络 (GAN) 类型的模型.
- 使用精细的身份采样策略和类似GAN的融合网络来增强在inpainting期间的身份保护.
主要成果:
- ID-Inpainter在基准数据集 (LFW,CFP-FP,AgeDB-30) 上展示了在身份保护中最先进的性能.
- 当标准面部识别器应用于ID-Inpainter处理的隐蔽图像时,其准确性得到了显著的改进.
结论:
- 拟议的ID-Inpainter有效地恢复了封闭的面部区域,同时保持了身份完整性.
- 这种方法大大提高了面部识别系统在存在遮时的稳定性和准确性.
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