High precision PSO and FLS integrated method for facial landmark localization

S. Khanmohammadi, S. M. Bakhshmand, Hadi Seyedarabi
{"title":"High precision PSO and FLS integrated method for facial landmark localization","authors":"S. Khanmohammadi, S. M. Bakhshmand, Hadi Seyedarabi","doi":"10.1109/FUZZY.2009.5276885","DOIUrl":null,"url":null,"abstract":"Automatic finding exact location of facial salient points under translation, rotation and changing lightning illumination is a considerable task in face image processing. This paper presents a multistage procedure for finding landmark points on a rigid object like human face. Gabor filter jets make EBGM, very effective but computationally expensive. In proposed method, searching landmark points using Gabor filter jets is optimized by using particle swarm optimization (PSO) and similarity between model jet and extracted jet as cost function. After locating first landmark, the location of next landmark is estimated and then is refined by local search criteria (FLS) until localizing of all desired 5 landmarks. Model jets are used for accounting pixels and can be extracted manually from landmark points of same identity for more robustness and accuracy. Results based on the proposed approach are included to prove the accuracy and low computational cost of proposed method comparing the exhaustive search.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE International Conference on Fuzzy Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FUZZY.2009.5276885","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Automatic finding exact location of facial salient points under translation, rotation and changing lightning illumination is a considerable task in face image processing. This paper presents a multistage procedure for finding landmark points on a rigid object like human face. Gabor filter jets make EBGM, very effective but computationally expensive. In proposed method, searching landmark points using Gabor filter jets is optimized by using particle swarm optimization (PSO) and similarity between model jet and extracted jet as cost function. After locating first landmark, the location of next landmark is estimated and then is refined by local search criteria (FLS) until localizing of all desired 5 landmarks. Model jets are used for accounting pixels and can be extracted manually from landmark points of same identity for more robustness and accuracy. Results based on the proposed approach are included to prove the accuracy and low computational cost of proposed method comparing the exhaustive search.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
高精度PSO与FLS相结合的人脸地标定位方法
在平移、旋转和闪电光照变化的情况下,自动找到面部突出点的精确位置是人脸图像处理中的一个重要课题。本文提出了一种在人脸等刚性物体上寻找地标点的多阶段算法。Gabor过滤器射流使EBGM,非常有效,但计算昂贵。该方法以粒子群算法(PSO)为代价函数,以模型射流与提取射流的相似性为代价函数,对Gabor滤波射流的地标点搜索进行优化。定位第一个地标后,估计下一个地标的位置,然后通过局部搜索标准(FLS)进行优化,直到定位到所有需要的5个地标。模型喷射用于计算像素,并且可以从相同身份的地标点手动提取,以获得更高的鲁棒性和准确性。通过与穷举搜索方法的比较,验证了该方法的准确性和较低的计算成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Design and simulation of a hybrid controller for a multi-input multi-output magnetic suspension system Fuzzy CMAC structures Hybrid SVM-GPs learning for modeling of molecular autoregulatory feedback loop systems with outliers On-line adaptive T-S fuzzy neural control for active suspension systems Analyzing KANSEI from facial expressions with fuzzy quantification theory II
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1