A novel approach for face privacy protection based on surveillance video in complex scene

Chi ZHANG; Ye LU; Yuping LUO; Xiaokai SUN; Hanke ZHU

doi:10.11959/j.issn.1000-0801.2021015

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A novel approach for face privacy protection based on surveillance video in complex scene

Research and Development | 更新时间：2024-06-05

- A novel approach for face privacy protection based on surveillance video in complex scene
- Telecommunications Science Vol. 37, Issue 1, Pages: 94-101(2021)
- 作者机构：
- 作者简介：
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- DOI：10.11959/j.issn.1000-0801.2021015
  CLC： TP311
- Published Online：2021-01，
  
  Published：20 January 2021
- 稿件说明：
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Chi ZHANG, Ye LU, Yuping LUO, et al. A novel approach for face privacy protection based on surveillance video in complex scene[J]. Telecommunications science, 2021, 37(1): 94-101.
DOI：

Chi ZHANG, Ye LU, Yuping LUO, et al. A novel approach for face privacy protection based on surveillance video in complex scene[J]. Telecommunications science, 2021, 37(1): 94-101. DOI： 10.11959/j.issn.1000-0801.2021015.

摘要

查看视频监控的过程中，一些场景存在因为人脸面部信息暴露在监控视频中导致个人隐私信息泄露的风险，有必要对实时视频流中的行人隐私信息进行马赛克处理。目前市面上常见的基于人脸检测的打码方法在实时监控视频流上打码效果受行人姿态、光线影响较大，存在实时性差、漏检较多等问题。针对以上问题，提出了融合人脸检测算法、目标物体检测算法和前置帧关联检测方法的多检测模型，并与传统的人脸检测模型进行对比。实验结果表明，在人脸检测召回率上，所提模型相较于传统人脸检测算法提高了532%。

Abstract

During the process of monitoring video surveillance

potential risks of leaking personal privacy information may occur due to the exposure of facial information to the surveillance video.It is necessary to mosaic the pedestrian privacy information in the real-time video stream.At present

the commonly used coding method based on face detection is heavily affected by pedestrian appearances and illumination

which often leads to weak real-time performance and undetected errors.To solve the problems above

a multi-detection model was proposed

combining face detection algorithm

target object detection algorithm and pre-frame association detection method

and compared with the traditional face detection model.Experimental results show that the recall rate of the proposed model is 532% higher than that of the traditional face detection algorithm.

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references

毕玉谦 , 洪霄 . 民事诉讼生成权利规制探析——以“人脸识别第一案”为切入点 [J ] . 法学杂志 , 2020 , 41 ( 3 ): 53 - 62 .

BI Y Q , HONG X . Research on regulations of generating rRights in civil litigation:based on the first case of facial recognition [J ] . Law Science Magazine , 2020 , 41 ( 3 ): 53 - 62 .

BRADSKI G , KAEHLER A . Learning openCV:computer vision with the openCV library [J ] . IEEE Robotics ＆ Automation Magazine , 2008 ( 9 ): 100 .

陈玲 . 人脸检测技术综述 [J ] . 科学与信息化 , 2018 ( 16 ): 1 -.

CHEN L . Overview of face detection technology [J ] . Technology and Information , 2018 ( 16 ): 1 .

侯小毛 , 徐仁伯 . 云环境中考虑隐私保护的人脸图像识别 [J ] . 沈阳工业大学学报 , 2018 ( 2 ): 203 - 207 .

HOU X M , XU R B . Face image recognition based on privacy protection in cloud environment [J ] . Journal of Shenyang University of Technology , 2018 ( 2 ): 203 - 207 .

张啸剑 , 付聪聪 , 孟小峰 . 面向人脸图像发布的差分隐私保护 [J ] . 中国图像图形学报 , 2018 , 23 ( 9 ): 1305 - 1315 .

ZHANG X J , FU C C , MENG X F . Facial image publication with differential privacy [J ] . Journal of Image and Graphics , 2018 , 23 ( 9 ): 1305 - 1315 .

PAPAGEORGIOU C P , OREN M , POGGIO T . A general framework for object detection [C ] // Proceedings of the International Conference on Computer Vision (ICCV) . Piscataway:IEEE Press , 1998 : 555 - 562 .

崔鹏 , 燕天天 . 融合YCbCr肤色模型与改进的Adaboost算法的人脸检测 [J ] . 哈尔滨理工大学学报 , 2018 ( 2 ): 91 - 96 .

CUI P , YAN T T . Face detection combining the YCbCr skin color model with improved adaboost algorithm [J ] . Journal of Harbin University of Science and Technology , 2018 ( 2 ): 91 - 96 .

CHEN P H , LIN C J , SCHLKOPF B . A tutorial on ν-support vector machines [J ] . Applied Stochastic Models in Business and Industry , 2005 , 21 ( 2 ): 111 - 136 .

ZHANG K , ZHANG Z , LI Z , et al . Joint face detection and alignment using multitask cascaded convolutional networks [J ] . IEEE Signal Processing Letters , 2016 , 23 ( 10 ): 1499 - 1503 .

陈正斌 , 叶东毅 , 朱彩霞 , 等 . 基于改进 YOLOv3 的目标识别方法 [J ] . 计算机系统应用 , 2020 , 29 ( 1 ): 49 - 58 .

CHEN Z B , YE D Y , ZHU C X , et al . Object recognition method based on improved YOLOv3 [J ] . Computer Systems Applications , 2020 , 29 ( 1 ): 49 - 58 .

LIN T Y , MAIRE M S , BELONGIE J , et al . Microsoft COCO:common objects in context [C ] // Proceedings of European Conference on Computer Vision .[S.l:s.n. ] , 2014 : 740 - 755 .

刘卫华 , 吴丹 . 基于 OpenCV 的人脸检测 [J ] . 科技创新与应用 , 2019 ( 31 ): 18 - 19 .

LIU W H , WU D . Face detection based on OpenCV [J ] . Technology Innovation and Application , 2019 ( 31 ): 18 - 19 .

金翠 , 王洪元 , 陈首兵 . 基于随机擦除行人对齐网络的行人重识别方法 [J ] . 山东大学学报:工学版 , 2018 , 48 ( 6 ): 71 - 77 .

JIN C , WANG H Y , CHEN S B . Person re-identification based on random erasing pedestrian alignment network method [J ] . Journal of Shandong University (Engineering Science) , 2018 , 48 ( 6 ): 71 - 77 .

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