基于特征融合的轻量级新残差人脸识别方法

惠康华; 闫建青; 高思华; 贺怀清

doi:10.12263/DZXB.20221024

您当前的位置：

首页 >

文章列表页 >

基于特征融合的轻量级新残差人脸识别方法

学术论文 | 更新时间：2025-12-11

- 基于特征融合的轻量级新残差人脸识别方法
- Lightweight New Fesidual Face Recognition Method Based on Feature Fusion
- 电子学报 2024年52卷第3期页码：937-944
- 作者机构：
  
  中国民航大学计算机科学与技术学院，天津 300300
- 作者简介：
  
  [ "惠康华男，1982年9月出生于江苏连云港市.现为中国民航大学计算机科学与技术学院副教授.主要研究方向为计算机视觉. E-mail: khhui@cauc.edu.cn" ]
  [ "闫建青男，1998年9月出生于山西忻州市.现为中国民航大学计算机科学与技术学院硕士研究生.主要研究方向为图像处理.E-mail: 2019051017@cauc.edu.cn" ]
  [ "高思华男，1987年1月出生于承德市.现为中国民航大学计算机科学与技术学院讲师.主要研究方向为强化学习理论及应用. E-mail: shgao@cauc.edu.cn" ]
  [ "贺怀清女，1969年11月出生于吉林白山市.现为中国民航大学计算机科学与技术学院教授.主要研究方向为图形图像与可视分析. E-mail: hqhe@cauc.edu.cn" ]
- 基金信息：
  
  国家重点研发计划项目(2020YFB1600101);天津市教委科研项目(2020KJ024)
- DOI：10.12263/DZXB.20221024
  中图分类号： TP391.41
- 收稿：2022-09-05，
  
  修回：2022-11-23，
  
  纸质出版：2024-03-25
- 稿件说明：
移动端阅览
惠康华,闫建青,高思华,等.基于特征融合的轻量级新残差人脸识别方法[J].电子学报,2024,52(03):937-944.

HUI Kang-hua, YAN Jian-qing, GAO Si-hua, et al.Lightweight New Fesidual Face Recognition Method Based on Feature Fusion[J].Acta Electronica Sinica, 2024, 52(03): 937-944.
惠康华,闫建青,高思华,等.基于特征融合的轻量级新残差人脸识别方法[J].电子学报,2024,52(03):937-944. DOI：10.12263/DZXB.20221024

HUI Kang-hua, YAN Jian-qing, GAO Si-hua, et al.Lightweight New Fesidual Face Recognition Method Based on Feature Fusion[J].Acta Electronica Sinica, 2024, 52(03): 937-944. DOI：10.12263/DZXB.20221024

摘要

针对现有轻量级模型在嵌入式设备的人脸识别应用中存在识别精度难以提升的问题，提出一种融合人脸对齐关键特征点信息的轻量级新残差网络模型（Lightweight New Residual Network， LNRN）.LNRN利用深度残差网络结构能够解决网络退化且避免干扰因素影响的优势，结合人脸对齐环节产生的关键特征点信息，对深度残差网络结构进行简化和合理设计，实现对关键特征信息和全局信息的提取.为避免特征提取过程中丢失重要特征信息，该模型在新残差网络中加入结合空间和通道的注意力机制进行辅助.在公开的四个标准人脸数据集上的仿真实验表明，该模型识别速度在接近主流轻量级人脸识别方法的同时，平均识别精度比MobiFace提高了0.6%.

Abstract

Aiming at the problem that the existing lightweight models are difficult to improve the recognition accuracy in the face recognition applications of embedded devices

a new lightweight residual network model (Lightweight New Residual Network

LNRN) that integrated the key feature point information of face alignment is proposed. The advantage of deep residual network structure that can solve the network degradation and avoid the influence of interference factors are absorbed by LNRN. In order to realize the extraction of key feature information and global information after combining the key point information generated by the face alignment

the deep residual network structure is simplified and reasonably designed. In order to avoid losing important feature information in the process of feature extraction

an attention mechanism combining space and channel is added to the new residual network for assistance. Simulation experiments on the four standard face datasets showed that the recognition speed of the proposed model was close to the mainstream lightweight face methods

and the average recognition accuracy of the proposed model is 0.6% higher than that of MobiFace.

关键词

Keywords

references

TAKALKAR M , XU M , WU Q , et al . A survey: Facial micro-expression recognition [J ] . Multimedia Tools and Applications , 2018 , 77 ( 15 ): 19301 - 19325 .

SHARMA R , PATTERH M S . A new hybrid approach using PCA for pose invariant face recognition [J ] . Wireless Personal Communications , 2015 , 85 ( 3 ): 1561 - 1571 .

TAO Q Q , ZHAN S , LI X H , et al . Robust face detection using local CNN and SVM based on kernel combination [J ] . Neurocomputing , 2016 , 211 : 98 - 105 .

LECUN Y , BENGIO Y , HINTON G . Deep learning [J ] . Nature , 2015 , 521 : 436 - 444 .

李倩玉 , 蒋建国 , 齐美彬 . 基于改进深层网络的人脸识别算法 [J ] . 电子学报 , 2017 , 45 ( 3 ): 619 - 625 .

LI Q Y , JIANG J G , QI M B . Face recognition algorithm based on improved deep networks [J ] . Acta Electronica Sinica , 2017 , 45 ( 3 ): 619 - 625 . (in Chinese)

WANG M , DENG W H . Deep face recognition: A survey [J ] . Neurocomputing , 2021 , 429 : 215 - 244 .

CHAN T H , JIA K , GAO S H , et al . PCANet: A simple deep learning baseline for image classification? [J ] . IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society , 2015 , 24 ( 12 ): 5017 - 5032 .

李小薪 , 梁荣华 . 有遮挡人脸识别综述: 从子空间回归到深度学习 [J ] . 计算机学报 , 2018 , 41 ( 1 ): 177 - 207 .

LI X X , LIANG R H . A review for face recognition with occlusion: From subspace regression to deep learning [J ] . Chinese Journal of Computers , 2018 , 41 ( 1 ): 177 - 207 . (in Chinese)

SCHROFF F , KALENICHENKO D , PHILBIN J . FaceNet: A unified embedding for face recognition and clustering [C ] // 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2015 : 815 - 823 .

PARKHI O M , VEDALDI A , ZISSERMAN A . Deep face recognition [ED/OL ] . ( 2015-09-10 )[ 2022-08-05 ] . https://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/parkhi15.pdf?utm_source=top.caibaojian.com/31386&from=weibo.com/kujian https://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/parkhi15.pdf?utm_source=top.caibaojian.com/31386&from=weibo.com/kujian .

LIU W Y , WEN Y D , YU Z D , et al . SphereFace: Deep hypersphere embedding for face recognition [C ] // 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2017 : 212 - 220 .

WANG H , WANG Y T , ZHOU Z , et al . CosFace: Large margin cosine loss for deep face recognition [C ] // 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2018 : 5265 - 5274 .

DENG J K , GUO J , XUE N N , et al . ArcFace: Additive angular margin loss for deep face recognition [C ] // 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2019 : 4690 - 4699 .

HE K M , ZHANG X Y , REN S Q , et al . Deep residual learning for image recognition [C ] // 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2016 : 770 - 778 .

汪宇玲 , 陈立 , 黎明 , 等 . 基于迹变换和旋转增量调制特征的模糊人脸识别 [J ] . 电子学报 , 2021 , 49 ( 12 ): 2437 - 2448 .

WANG Y L , CHEN L , LI M , et al . Rotational delta modulation feature and its application in blurry face recognition based on trace transform [J ] . Acta Electronica Sinica , 2021 , 49 ( 12 ): 2437 - 2448 . (in Chinese)

KIM M , JAIN A K , LIU X M . AdaFace: Quality adaptive margin for face recognition [C ] // 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2022 : 18750 - 18759 .

WANG K , WANG S , ZHANG P P , et al . An efficient training approach for very large scale face recognition [C ] // 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2022 : 4083 - 4092 .

吴长虹 , 苏剑波 , 陈叶飞 . 抗年龄干扰的人脸识别 [J ] . 电子学报 , 2018 , 46 ( 7 ): 1593 - 1600 .

WU C H , SU J B , CHEN Y F . Age invariant face recognition [J ] . Acta Electronica Sinica , 2018 , 46 ( 7 ): 1593 - 1600 . (in Chinese)

HU J , SHEN L , SUN G . Squeeze-and-excitation networks [C ] // 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2018 : 7132 - 7141 .

SANDLER M , HOWARD A , ZHU M L , et al . MobileNetV2: Inverted residuals and linear bottlenecks [C ] // 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2018 : 4510 - 4520 .

ZHANG X Y , ZHOU X Y , LIN M X , et al . ShuffleNet: An extremely efficient convolutional neural network for mobile devices [C ] // 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway : IEEE , 2018 : 6848 - 6856 .

WOO S , PARK J , LEE J Y , et al . CBAM: Convolutional block attention module [C ] // Computer Vision—ECCV 2018 . Cham : Springer International Publishing , 2018 : 3 - 19 .

万俊 , 李晶 , 常军 , 等 . 基于局部形状组合模型的人脸对齐 [J ] . 计算机学报 , 2018 , 41 ( 9 ): 2162 - 2174 .

WAN J , LI J , CHANG J , et al . Face alignment on local-shape-based combined model [J ] . Chinese Journal of Computers , 2018 , 41 ( 9 ): 2162 - 2174 . (in Chinese)

李骜 , 王卓 , 于晓洋 , 等 . 多核低冗余表示学习的稳健多视图子空间聚类方法 [J ] . 通信学报 , 2021 , 42 ( 11 ): 193 - 204 .

LI A , WANG Z , YU X Y , et al . Robust multiview subspace clustering method based on multi-kernel low-redundancy representation learning [J ] . Journal on Communications , 2021 , 42 ( 11 ): 193 - 204 . (in Chinese)

YI D , LEI Z , LIAO S C , et al . Learning face representation from Scratch [EB/OL ] . ( 2014-11-28 )[ 2022-08-05 ] . https://arxiv.org/pdf/1411.7923.pdf https://arxiv.org/pdf/1411.7923.pdf .

HUANG G B , MATTAR M , BERG T , et al . Labeled faces in the wild: A database for Studying face recognition in unconstrained environments [EB/OL ] . ( 2008-07-01 )[ 2022-08-05 ] . https://vis-www.cs.umass.edu/lfw/lfw.pdf https://vis-www.cs.umass.edu/lfw/lfw.pdf .

MOSCHOGLOU S , PAPAIOANNOU A , SAGONAS C , et al . AgeDB: The first manually collected, In-the-wild age database [C ] // 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) . Piscataway : IEEE , 2017 : 1997 - 2005 .

SENGUPTA S , CHEN J C , CASTILLO C , et al . Frontal to profile face verification in the wild [C ] // 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) . Piscataway : IEEE , 2016 : 1 - 9 .

ZHANG K P , ZHANG Z P , LI Z F , et al . Joint face detection and alignment using multitask cascaded convolutional networks [J ] . IEEE Signal Processing Letters , 2016 , 23 ( 10 ): 1499 - 1503 .

DUONG C N , QUACH K G , JALATA I , et al . MobiFace: A lightweight deep learning face recognition on mobile devices [C ] // 2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS) . New York : ACM , 2019 : 1 - 6 .

HAN K , WANG Y H , TIAN Q , et al . GhostNet: More features from cheap operations [C ] // 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2020 : 1580 - 1589 .

SUN K , XIAO B , LIU D , et al . Deep high-resolution representation learning for human pose estimation [C ] // 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE , 2019 : 5693 - 5703 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于邻域与超图协作的会话推荐

基于EIMYOLO的高分遥感图像目标检测

基于多重注意力和感知加权学习的单图像高动态范围重建

面向不同挑战及同异质信息分离的RGBT跟踪