Gender Effect on Face Recognition for a Large Longitudinal Database
Gender Effect on Face Recognition for a Large Longitudinal Database
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DOI：
10.1109/wifs.2018.8630762
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发表时间：
2018-11
期刊：
影响因子：
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通讯作者：
Caroline Werther;M. Ferguson;K. Park;T. Kling;Cuixian Chen;Yishi Wang
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文献类型：
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作者：
Caroline Werther;M. Ferguson;K. Park;T. Kling;Cuixian Chen;Yishi Wang
年龄或性别变化会显著影响人脸识别性能。虽然大多数人脸识别研究都集中在姿势、光照和表情的变化上，但考虑性别效应的影响以及如何设计有效的匹配框架是很重要的。在本文中，我们在一个非常大的纵向数据库Morph-II上解决了这些问题，该数据库包含13,617个人的55,134张人脸图像。首先，我们考虑了四个不同性别分布和子集大小组合的综合实验，包括：1)平均性别分布；2)大的高度不平衡的性别分布；3)考虑不同的性别组合，如纯男性、纯女性或混合性别；以及4)子集大小对个体数量的影响。其次，我们考虑了八个最近邻距离度量，并将支持向量机用于分类器，并测试了不同分类器的效果。最后，我们考虑了不同的融合技术为有效的匹配框架，以提高识别性能。
Aging or gender variation can affect the face recognition performance dramatically. While most of the face recognition studies are focused on the variation of pose, illumination and expression, it is important to consider the influence of gender effect and how to design an effective matching framework. In this paper, we address these problems on a very large longitudinal database MORPH-II which contains 55,134 face images of 13,617 individuals. First, we consider four comprehensive experiments with different combination of gender distribution and subset size, including: 1) equal gender distribution; 2) a large highly unbalanced gender distribution; 3) consider different gender combinations, such as male only, female only, or mixed gender; and 4) the effect of subset size in terms of number of individuals. Second, we consider eight nearest neighbor distance metrics and also Support Vector Machine (SVM) for classifiers and test the effect of different classifiers. Last, we consider different fusion techniques for an effective matching framework to improve the recognition performance.