Human personality privacy analysis based on visual features

Jie NIE; Lei HUANG; Zhen LI; Jian ZHANG; Yan YAN; Zhi-qiang WEI

doi:10.11959/j.issn.1000-436x.2016223

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Human personality privacy analysis based on visual features

academic paper | 更新时间：2024-06-05

- Human personality privacy analysis based on visual features
- Journal on Communications Vol. 37, Issue 11, Pages: 129-136(2016)
- 作者机构：
  
  1. 清华大学计算机科学与技术系，北京 100084
  2. 中国海洋大学信息科学与工程学院，山东青岛 266100
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61672475);The National Natural Science Foundation of China(61402428);The National Natural Science Foundation of China(61602430)
- DOI：10.11959/j.issn.1000-436x.2016223
  CLC： TP37
- Online First：2016-11，
  
  Published：25 November 2016
- 稿件说明：
移动端阅览
Jie NIE, Lei HUANG, Zhen LI, et al. Human personality privacy analysis based on visual features[J]. Journal on Communications, 2016, 37(11): 129-136.
DOI：

Jie NIE, Lei HUANG, Zhen LI, et al. Human personality privacy analysis based on visual features[J]. Journal on Communications, 2016, 37(11): 129-136. DOI： 10.11959/j.issn.1000-436x.2016223.

摘要

社交媒体中的个性化推荐试图通过对公开在公众平台中的用户位置、图片等信息的分析得到用户的习惯、性格等，以提供个性化服务，但也因此给用户的隐私安全造成威胁。从隐私挖掘的角度，提出了一种基于人物图像的人物性格隐私分析方法。基于认知科学中的人物性格模型，提出了5类视觉特征，包括颜色、纹理、形状、伊顿对比和表情特征。实验表明，所提方法可以有效挖掘用户的性格隐私，特征选择实验进一步验证了所提出特征的有效性。

Abstract

Personalized recommendation in social media attempts to analyze the habits and personality based on the loca-tion information and pictures from the users' comments on the public platform. This is very useful for personalized in-formation recommendation. But from perspective of privacy protection

this will affect the privacy security. A novel hu-man personality privacy analysis method based on portrait was proposed. By analyzing the personality model of the psy-chology

five visual features were proposed

including color features

texture features

shape features

Itten contrast fea-tures and expression features. Comprehensive experiments demonstrate the effectiveness of the proposed method. Further

the features analysis experiment show that the proposed features are very relevant to human personality privacy analyz-ing.

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references

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