在创建简单的假视频时,高效的人脸检测和替换

Sheremet Oleksii I., Sadovoi Oleksandr V., Harshanov Denys V., Kovalchuk Oleh S., Sheremet Kateryna S., Sokhina Yuliia V.
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引用次数: 0

摘要

人脸检测和人脸识别技术是计算机视觉领域中研究最深入的课题之一,因为它们在众多行业中具有巨大的应用潜力。这些技术已经证明了在各种情况下的实际应用适应性,例如在拥挤的城市空间中识别可疑人员,智能手机用户的实时识别,为娱乐应用程序创建引人注目的深度伪造,以及修改面部特征(如嘴唇或眼睛)运动的专门应用程序。随着当前最先进的硬件和软件技术的进步,今天的技术基础设施提供了比视频流所需的更多的资源。因此,简单的人脸识别系统可以在不需要高成本的服务器实例的情况下实现,这些服务器实例需要指定的预训练模型。这种丰富的资源正在改变人脸识别的格局,本文将围绕这些新兴范例进行讨论。本文的主要重点是使用突出的预训练模型对流视频数据中人脸检测的关键概念进行深入分析。正在讨论的模型包括HRNet, RetinaFace, Dlib, MediaPipe和KeyPoint R-CNN。每种模型都有其优缺点,本文将在实际案例研究的上下文中讨论这些属性。这个讨论提供了对这些模型的实际应用的有价值的见解,以及在它们的使用中所涉及的权衡。此外,本文还对图像变换技术进行了全面的综述。介绍了一种仿射图像变换的抽象方法,仿射图像变换是图像处理中的一项重要技术,它在不影响图像像素强度的情况下改变图像的几何特性。此外,本文还讨论了通过OpenCV库执行的图像转换操作,OpenCV库是计算机视觉领域的领先库之一,为图像操作提供了高度灵活和高效的工具集。本研究的最终成果是一个实用的视频图像替换独立系统。该系统利用RetinaFace模型进行推理,并采用OpenCV进行仿射变换,演示了本文讨论的概念和技术。因此,本文概述的工作推进了人脸检测和识别领域,提出了一种充分利用当代硬件和软件进步的创新方法
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Efficient face detection and replacement in the creationofsimple fake videos
Face detection and facial recognition technologies are among the most intensively studied topics within the field of computervision, owing to their vast application potential across a multitude of industries. These technologies have demonstrated practicalap-plicability in varied contexts such as identifying suspicious individuals in crowded urban spaces, real-time recognition of smartphone owners, creating compelling deepfakes for entertainment applications, and specialized applications that modify the movements of facial features such as the lips or eyes. With the current state-of-the-art advancements in hardware and software technology, today's technological infrastructure provides more resources than are necessary for video streaming. As a result, simpleface recognition systems can be implemented without the need for high-cost server instances that require specified pre-trained models. This abun-dance of resources is changing the landscape of face recognition, and the discussion within this paper will revolve around these emerging paradigms.The primary focus of this article is an in-depth analysis of the key concepts of face detection in streaming video data using prominent pre-trained models. The models under discussion include HRNet, RetinaFace, Dlib, MediaPipe, and KeyPoint R-CNN. Each of these models has its strengths and weaknesses, and the article discusses these attributes in the context of real-world case studies. This discussion provides valuable insights into the practical applications of these models and the trade-offs involved in their utilization.Moreover, this paper presents a comprehensive overview of image transformation techniques. It introduces an ab-stract method for affine image transformation, animportanttechnique in image processing that changes the geometric properties of an image without affecting its pixel intensity. Additionally, the article discusses image transformation operations executed through the OpenCV library, one of the leading libraries in the field of computer vision, providing a highly flexible and efficient toolset for image manipulation.The culmination of this research is presented as a practical standalone system for image replacement in video. This system leverages the RetinaFace model for inference and employs OpenCV for affine transformations, demonstrating the con-cepts and technologies discussed in the paper. The work outlined in this article thereby advances the field of face detectionand recognition, presenting an innovative approach that makes full use of contemporary hardware and software advances
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