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历史模糊基于视频的面部识别技术

Lujun Zhai¹, Suxia Cui¹, Yonghui Wang²

¹Electrical and Computer Engineering Department, Prairie View A&M University, Prairie View, TX 77446, USA.

Journal of imaging

|September 27, 2024

概括

这项研究引入了用于历史电影的新型面部识别系统,提高了低分辨率图像的准确性. 联合人脸识别和跟踪网络显著减少了在具有挑战性的历史镜头中错过的检测.

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
数字图像处理数字图像处理

背景情况:

面部识别对于安全和身份验证至关重要,通常使用高清数字摄像头.
历史电影在面部检测方面面临着独特的挑战,因为它们的分辨率和图像质量较低.

研究的目的:

为历史动画电影开发一个有效的面部识别系统.
解决历史镜头中低分辨率和图像退化所带来的挑战.

主要方法:

提出了一个干枝连接的多任务级联卷积神经网络 (TB-MTCNN) 进行高效的面部特征提取.
集成了一个基于深度神经网络的对象跟踪算法 (Deep SORT与ResNet),以处理对识别失败.
使用最先进的图像恢复技术来减轻噪音和模糊.

主要成果:

TB-MTCNN有效地从模糊的历史电影图像中提取面部特征.
集成的跟踪算法弥补了视频中错过的识别.
综合方法显著减少了历史电影序列中错过的面部识别实例.

结论:

开发的联合面部识别和跟踪网络在历史电影中表现出卓越的性能.
这种方法为分析和提取从退化视觉档案中获取信息提供了强大的解决方案.

关键词:

面部检测面部检测面部检测面部识别系统是面部识别系统.面部跟踪系统面部跟踪系统历史模糊的视频历史模糊的视频

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