A Sparse Coding-based Method for Facial Feature Point Detection

TAN Shu-qiu; CHEN Dong-yi; GUO Cheng-gang; HUANG Zhi-qi; XIONG Xiao-feng

Chinese

您当前的位置：

首页 >

文章列表页 >

A Sparse Coding-based Method for Facial Feature Point Detection

更新时间：2026-09-04

- A Sparse Coding-based Method for Facial Feature Point Detection
- Vol. 34, Issue 9, Pages: 7-10(2017)
- 作者机构：
  
  电子科技大学自动化工程学院,四川,成都,611731
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2017
- 稿件说明：
移动端阅览
谭暑秋, 陈东义, 郭承刚, 黄志奇, 熊小锋. 一种基于稀疏编码的人脸特征点检测方法[J]. 微电子学与计算机, 2017, 34(9): 7-10, 14.

TAN Shu-qiu, CHEN Dong-yi, GUO Cheng-gang, et al. A Sparse Coding-based Method for Facial Feature Point Detection[J]. 2017, 34(9): 7-10.
谭暑秋, 陈东义, 郭承刚, 黄志奇, 熊小锋. 一种基于稀疏编码的人脸特征点检测方法[J]. 微电子学与计算机, 2017, 34(9): 7-10, 14. DOI：

TAN Shu-qiu, CHEN Dong-yi, GUO Cheng-gang, et al. A Sparse Coding-based Method for Facial Feature Point Detection[J]. 2017, 34(9): 7-10. DOI：

摘要

为了能较好地检测遮挡的人脸形状

提出了一种基于稀疏编码的人脸特征点检测方法.在该方法中

采用基于级联回归的思想

用基于稀疏约束的重构模型来迭代搜索人脸特征点位置.首先

利用训练集中的图像学习一个具有全局性以及通用性为特点的稀疏字典

然后结合人脸特征点局部区域内的纹理信息

使用支持向量回归的方法

重构出每次迭代所需的差分形状.为了验证该方法的可行性

将提出的方法在三个公开的数据集上进行测试.进而

为了进一步验证该方法的有效性

将该方法与两个性能较好的方法进行比较.实验结果表明所提方法具有可行性

且对遮挡的人脸特征点具有较高的识别率.

Abstract

In order to better detect the occlusion of the face shape

this paper propose a sparse coding-based method for facial feature point detection. This method adopts the idea of cascade regression

and uses the reconstruction model based on sparse constraint to iteratively find the positions of facial feature point. Firstly

a sparse dictionary is obtained during training stage

which is global and universal. Then

using the support vector regression and combining the texture information in the local region of the facial feature points

the method can reconstruct differential shape for each iteration. In order to verify the feasibility and effectiveness

this method is tested on three public face datasets

and compares with two methods. The experimental results show that our proposed method is feasible and the recognition rate of occluded face feature points is high.

关键词

Keywords

references

Cao X, Wei Y, Wen F, et al. Face alignment by explicit shape regression[J]. International Journal of Computer Vision, 2014, 107(2): 177-190. DOI: 177

Cristinacce D, Cootes T F. Feature detection and tracking with constrained local models[C]//Proc. Brit. Mach. Vis. Conf., Edinburgh, UK, 2006(2): 929-938.

Basak D, Pal S, Patranabis D C. Support vector regression[J]. Neural Information Processing-Letters and Reviews, 2007, 11(10): 203-224. DOI: 203

Saragih J, Goecke R. A nonlinear discriminative approach to aam fitting[C]//Proceedings of the IEEE International Conference on Computer Vision. Rio de Janeiro, Brazil, 2007: 1-8.

Burgos-Artizzu X, Perona P, Dollar P. Robust face landmark estimation under occlusion[C]//Proceedings of the IEEE International Conference on Computer Vision, 2013. Washington, 2013: 1513-1520.

Belhumeur P, Jacobs D, Kriegman D, et al. Localizing parts of faces using a consensus of exemplars[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. South Carolina, 2011: 545-552.

Dollár P, Welinder P, Perona P. Cascaded pose regression[C]//Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on. San Francisco, CA, USA, IEEE, 2010: 1078-1085.

Yan J, Lei Z, Yi D, et al. Learn to combine multiple hypotheses for accurate face alignment[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops. Barcelona, Spain, 2013: 392-396.

Le V, Brandt J, Lin Z, et al. Interactive facial feature localization[C]//Proceedings of European Conference on Computer Vision. Crete, Greece, 2012: 679-692.

Cootes T F, Edwards G J, Taylor C J. Active appearance models[J]. IEEE Transactions on pattern analysis and machine intelligence, 2001, 23(6): 681-685. DOI: 681

Xiong X, De F la Torre. Supervised descent method and its applications to face alignment[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. Portland, Oregon, 2013: 532-539.

蒋可心, 于鸿洋. 基于摄像头PTZ控制的人脸识别[J]. 微电子学与计算机, 2014, 31(4): 48-51. DOI: 48

覃磊, 李德华, 周康. 基于QR分解与2DLDA的单样本人脸识别[J]. 微电子学与计算机, 2015, 32(2): 65-68. DOI: 65

覃磊, 李德华, 周康. 基于分块2DPCA与2DLDA的单训练样本人脸识别[J]. 微电子学与计算机, 2015, 32(11): 105-110. DOI: 105

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Microservices anomaly detection based on the correlation of monitoring metrics

Design of ultra-low power real-time clock circuits

Simulation on the DC characteristics of double heterojunction p-GaN HEMT with AlN/GaN hybrid buffer layer

A high-efficiency charge pump for ultra-low voltage boost systems

Implementation and verification of a dual-chain power gating strategy for low power design

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰