Abstract
Face pixelation in TV shows or videos is manually realized and not well studied to date. As the prevailing of online video streaming, we develop a new tool called face pixelation in live-streaming (FPLV) to generate automatic personal privacy filtering during unconstrained streaming. FPLV is organized in a frame-to-video structure for fast and accurate face pixelation of irrelevant people. Leveraging image-based face detection and recognition networks on individual frames, we propose a positioned incremental affinity propagation (PIAP) clustering algorithm to associate faces across frames. Through deep feature and position aggregated affinities, PIAP handles the cluster number generation, new cluster discovering, and faces' raw trajectories forming simultaneously. Affected by various factors, raw trajectories might be intermittent and unreliable. Hence, we introduce a proposal net for loosed face detection with an empirical likelihood test to compensate the deep network insufficiency and refine the raw trajectories. A Gaussian filter is laid on refined trajectories for final pixelation. FPLV obtains satisfying accuracy and real-time performances under streaming video data we collected.
Abstract (translated)
电视节目或视频中的人脸像素化是手动实现的，目前还没有很好的研究。随着在线视频流的流行，我们开发了一种新的工具，叫做实时流中的人脸像素化（fplv），在无约束流中生成自动的个人隐私过滤。fplv被组织在一个帧到视频结构中，以便快速准确地对无关人员进行面部像素化。利用基于图像的人脸检测和识别网络，提出了一种定位增量式相似性传播（PIAP）聚类算法，实现了人脸跨帧关联。PIAP通过深度特征和位置聚集的亲合性，同时处理簇数生成、新簇发现和人脸的原始轨迹形成。受各种因素影响，原始轨迹可能是间歇性的、不可靠的。为此，我们引入了一个基于经验似然检验的松散人脸检测建议网，以弥补深层网络的不足，细化原始轨迹。高斯滤波器被放置在精确的轨迹上以进行最终的像素化。在所采集的流视频数据下，该算法获得了令人满意的精度和实时性。
URL
https://arxiv.org/abs/1903.10836
