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基于无约束和关节式人体的性别识别。

Gender recognition from unconstrained and articulated human body.

作者信息

Wu Qin, Guo Guodong

机构信息

Department of Computer Science, Jiangnan University, Wuxi, Jiangsu 214122, China ; Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA.

Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA.

出版信息

ScientificWorldJournal. 2014;2014:513240. doi: 10.1155/2014/513240. Epub 2014 Apr 7.

DOI:10.1155/2014/513240
PMID:24977203
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3996991/
Abstract

Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition, is also presented. This paper also pursues data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases which have not been explored before for gender recognition.

摘要

性别识别有许多有用的应用，从商业智能到图像搜索和社会活动分析。传统的性别识别研究集中在受限环境下的面部图像。本文提出了一种在现实世界中从非受限环境获取的人体关节图像中进行性别识别的方法。还对基于身体的性别识别中的一些关键问题进行了系统研究，比如哪些身体部位具有信息性、需要组合多少身体部位以及哪些表示法有利于基于人体关节的性别识别。本文还基于偏最小二乘估计研究了数据融合方案和有效的特征降维。在两个之前未用于性别识别的非受限数据库上进行了大量实验。

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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2824/3996991/aefeefdf5b89/TSWJ2014-513240.008.jpg

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本文引用的文献

1
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