基于度量学习和典型相关分析的亲缘关系识别网络
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辽宁工程技术大学研究生学院

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TP273

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国家自然科学基金项目(面上项目,重点项目,重大项目)


kinship relationship recognition network based on metric learning and canonical correlation analysis
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Liaoning Technology University Graduate School

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    摘要:

    目前存在识别亲缘关系的方法大多数针对单亲亲缘关系(父子关系、父女关系、母子关系、母女关系)识别,但这些识别方法对年龄差距大、性别不同的父女或母子的识别效果不佳,解决这种问题最佳方法同时处理孩子与父母之间的关系(双亲亲缘关系)识别。目前针对双亲亲缘关系的识别方法极少,存在一定提升的空间。为了提高识别子女与双亲之间亲缘关系的准确度,本文提出一种基于度量学习与相关分析的识别双亲亲缘关系模型。本文依据子女与双亲的生物遗传关系设计可融合子女与双亲的亲缘特征的多线性并行网络;并利用判别式度量学习和典型相关性分析在数据处理中优势,从包含多种人体身份的面部特征中提取有利于亲缘关系识别的特征信息用于鉴别子女与父母是否存在血缘关系以实现识别精度的提高。实验表明,该方法在子女与双亲的亲缘关系识别上识别效果更好。

    Abstract:

    At present, most of the methods for identifying the relationship of the single parent (father-son relationship, father-daughter relationship, mother-son relationship, mother-daughter relationship) , however, these methods are not effective for identifying father-daughter or mother-son with large age difference and different sex. The best way to solve this problem is to deal with the relationship between children and parents (the relationship between parents) . At present, there are few methods to identify parental relationship, and there is room for improvement. In order to improve the accuracy of identifying the relationship between children and their parents, a model based on metric learning and correlation analysis is proposed. Based on the biological genetic relationship between children and their parents, this paper designs a multi-linear parallel network which can fuse the genetic characteristics of children and their parents, and makes use of discriminant measure learning and canonical correlation analysis to gain advantage in data processing, in order to improve the recognition accuracy, the feature information which is helpful to identify the blood relationship between children and their parents is extracted from facial features which contain many kinds of human identities. Experiments show that this method is more effective in identifying the relationship between children and their parents.

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历史
  • 收稿日期:2019-12-23
  • 最后修改日期:2020-05-11
  • 录用日期:2020-05-12
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