使用随机森林的儿童纵向人脸识别

S. R., D. S. Guru, Manjunath Aradhya, Anitha Raghavendra
{"title":"使用随机森林的儿童纵向人脸识别","authors":"S. R., D. S. Guru, Manjunath Aradhya, Anitha Raghavendra","doi":"10.2139/ssrn.3735819","DOIUrl":null,"url":null,"abstract":"The children face recognition system plays a vital role in application towards the track and recognize the children who are missing at a young age due to child trafficking or kidnapping. In this study, an attempt is made to find the rate of recognition of a young child face images from age 1 to 15 years, where the frequency of facial growth is higher in this interval. To study the effectiveness of the problem, a dimensionality reduction technique is adopted such as PCA followed by random forest classifier as one method and deep convolutional neural network as another method. The proposed model was vindicated with a relatively large dataset that was created to address this problem. The total number of longitudinal face images of 47 children each 12-15 years was 685 and each year consists of a single sample. Experimentation was carried out by varying the number of decision trees and the number of classes for efficacious analysis of the problem. The procured results were promising with deep learning classification techniques with 93% of mini-batch accuracy.","PeriodicalId":18268,"journal":{"name":"Materials Engineering eJournal","volume":"71 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Children Longitudinal Face Recognition Using Random Forest\",\"authors\":\"S. R., D. S. Guru, Manjunath Aradhya, Anitha Raghavendra\",\"doi\":\"10.2139/ssrn.3735819\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The children face recognition system plays a vital role in application towards the track and recognize the children who are missing at a young age due to child trafficking or kidnapping. In this study, an attempt is made to find the rate of recognition of a young child face images from age 1 to 15 years, where the frequency of facial growth is higher in this interval. To study the effectiveness of the problem, a dimensionality reduction technique is adopted such as PCA followed by random forest classifier as one method and deep convolutional neural network as another method. The proposed model was vindicated with a relatively large dataset that was created to address this problem. The total number of longitudinal face images of 47 children each 12-15 years was 685 and each year consists of a single sample. Experimentation was carried out by varying the number of decision trees and the number of classes for efficacious analysis of the problem. The procured results were promising with deep learning classification techniques with 93% of mini-batch accuracy.\",\"PeriodicalId\":18268,\"journal\":{\"name\":\"Materials Engineering eJournal\",\"volume\":\"71 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Materials Engineering eJournal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3735819\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materials Engineering eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3735819","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

儿童人脸识别系统在追踪和识别因拐卖或绑架而导致的年幼失踪儿童方面具有重要的应用价值。在本研究中,我们试图找出1 - 15岁儿童面部图像的识别率,在这个年龄段,面部生长的频率更高。为了研究问题的有效性,采用了PCA +随机森林分类器和深度卷积神经网络等降维技术。为了解决这个问题而创建的一个相对较大的数据集证明了所提出的模型是正确的。每12-15岁的47名儿童的纵向面部图像总数为685张,每年由一个样本组成。通过改变决策树的数量和类的数量来进行实验,以有效地分析问题。使用深度学习分类技术获得的结果很有希望,具有93%的小批量准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Children Longitudinal Face Recognition Using Random Forest
The children face recognition system plays a vital role in application towards the track and recognize the children who are missing at a young age due to child trafficking or kidnapping. In this study, an attempt is made to find the rate of recognition of a young child face images from age 1 to 15 years, where the frequency of facial growth is higher in this interval. To study the effectiveness of the problem, a dimensionality reduction technique is adopted such as PCA followed by random forest classifier as one method and deep convolutional neural network as another method. The proposed model was vindicated with a relatively large dataset that was created to address this problem. The total number of longitudinal face images of 47 children each 12-15 years was 685 and each year consists of a single sample. Experimentation was carried out by varying the number of decision trees and the number of classes for efficacious analysis of the problem. The procured results were promising with deep learning classification techniques with 93% of mini-batch accuracy.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信