计算机科学 ›› 2019, Vol. 46 ›› Issue (6A): 274-278.
韩旭1, 谌海云1, 王溢2, 许瑾1
HAN Xu1, CHEN Hai-yun1, WANG Yi2, XU Jin1
摘要: 基于单样本的人脸识别是一项充满挑战性的任务。文中结合Similar Principal Component Analysis(SPCA)算法与Histograms of Oriented Gradients(HOG)算法,利用SPCA筛选出图像类的相似信息,用HOG算法对相似的信息块进行特征量化,使二者优势互补。最后利用Pearson correlation(PC)进行相似性判别,在数据库Extended Yale B database上进行实验,结果表明,在光照变化的情况下,该算法对人脸正面图像的识别性能比传统算法好。
中图分类号:
[1]左腾.人脸识别技术综述[J].软件导刊,2017(2):182-185. [2]肖梦佳.基于图像识别的施工现场智能监控系统的相关技术研究与实现[D].成都:电子科技大学,2016. [3]马园园.人脸识别技术与考勤系统应用研究[D].南京:南京邮电大学,2017. [4]何志威,李军.基于人脸识别的移动终端考勤系统的设计[J].福建电脑,2018,34(3):19-20. [5]杨秀坤,岳新启,汲清波.基于HOG和DMMA的单样本人脸识别[J].计算机应用研究,2015(2):627-629. [6]杨恢先,翟云龙,蔡勇勇,等.基于中心对称梯度幅值相位模式的单样本人脸识别[J].光电子·激光,2015(5):969-977. [7]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(2):65-68. [8]YANG M,VAN L,ZHANG L.Sparse Variation Dictionary Learning for Face Recognition with a Single Training Sample per Person[C]∥2013 IEEE International Conference on Computer Vision(ICCV).IEEE,2013:689-696. [9]韩旭,刘强,许瑾,谌海云.基于伪PCA的手写数字识别算法[J].计算机科学,2018,45(S2):278-281,307. [10]DALAL,NAVNEET,TRIGGS,et al.Histograms of Oriented Gradients for Human Detection[C]∥IEEE International Conference on Computer Vision and Pattern Recognition,CVPR 2005.2005:886-893. [11]RAHMAN N A.A course in theoretical statistics for sixth forms,technical colleges,colleges of education,universities[M].Charles Griffin & Company Limited,1969. [12]BUDA A,JARYNOWSKI A.Life time of correlations and its applications[M].ABRASCO-Associação Brasileira de Saúde Coletiva,2010:459-470. [13]https://en.wikipedia.org/wiki/Pearson_correlation_coefficient. [14]http://www.realstatistics.com/correlation/basic-concepts-correlation. [15]JR J S.The Relationship between the Coefficient of Correlation and the Angle included between Regression Lines[J].Journal of Educational Research,1947,41(4):311-313. [16]http://www.hawaii.edu/powerkills/UC.HTM. [17]LEE K C,HO J,KRIEGMAN D J.Acquiring Linear Subspaces for Face Recognition under Variable Lighting[J].IEEE Tran-sactions on Pattern Analysis & Machine Intelligence,2005,27(5):684-698. [18]GEORGHIADES A S,BELHUMEUR P N,KRIEGMAN D J.From few to many:illumination cone models for face recognition under variable lighting and pose[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2002,23(6):643-660. [19]CORTES C,VAPNIK V.Support vector machines[J].Machine Learning,1995,20:273-293. [20]COVER T,HART P.Nearest neighbor pattern classification [J].IEEE Trans.inf.theory,1967,13(1):21-27. [21]WRIGHT J,YANG A Y,GANESH A,et al.Robust face recognition via sparse representation[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2008,31(2):210-227. [22]LOUGHERY J.Making FLDA applicable to face recognition with one sample per person[J].Pattern Recognition,2004,37(7):1553-1555. [23]ZHANG L,YANG M,FENG X.Sparse representation or collaborative representation:Which helps face recognition?[C]∥IEEE International Conference on Computer Vision(ICCV 2011).Bacelona,Spain:IEEE,2011:471-478. [24]SU Y,SHAN S,CHEN X,et al.Adaptive generic learning for face recognition from a single sample per person[C]∥Computer Vision and Pattern Recognition.IEEE,2010:2699-2706. [25]LU J,TAN Y P,WANG G.Discriminative Multimanifold Ana-lysis for Face Recognition from a Single Training Sample per Person[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2012,35(1):39-51. [26]KUMAR R,BANERJEE A,VEMURI B C,et al.Maximizing all margins:Pushing face recognition with Kernel Plurality[C]∥International Conference on Computer Vision.IEEE,2011:2375-2382. [27]DENG W,HU J,GUO J.Extended SRC:Undersampled Face Recognition via Intraclass Variant Dictionary[J].IEEE Tran-sactions on Pattern Analysis & Machine Intelligence,2012,34(9):1864-1870. [28]ZHU P,ZHANG L,HU Q,et al.Multi-scale Patch Based Collaborative Representation for Face Recognition with Margin Distribution Optimization[C]∥European Conference on Computer Vision.IEEE,2012:822-835. [29]ZHU P,YANG M,ZHANG L,et al.Local Generic Representation for Face Recognition with Single Sample per Person[M].Springer International Publishing,2014:41-50. |
[1] | 黄璞, 杜旭然, 沈阳阳, 杨章静. 基于局部正则二次线性重构表示的人脸识别 Face Recognition Based on Locality Regularized Double Linear Reconstruction Representation 计算机科学, 2022, 49(6A): 407-411. https://doi.org/10.11896/jsjkx.210700018 |
[2] | 黄璞, 沈阳阳, 杜旭然, 杨章静. 基于局部约束特征线表示的人脸识别 Face Recognition Based on Locality Constrained Feature Line Representation 计算机科学, 2022, 49(6A): 429-433. https://doi.org/10.11896/jsjkx.210300169 |
[3] | 胡聪, 何晓晖, 邵发明, 张艳武, 卢冠林, 王金康. 基于极大极稳定区域及SVM的交通标志检测 Traffic Sign Detection Based on MSERs and SVM 计算机科学, 2022, 49(6A): 325-330. https://doi.org/10.11896/jsjkx.210300117 |
[4] | 程祥鸣, 邓春华. 基于无标签知识蒸馏的人脸识别模型的压缩算法 Compression Algorithm of Face Recognition Model Based on Unlabeled Knowledge Distillation 计算机科学, 2022, 49(6): 245-253. https://doi.org/10.11896/jsjkx.210400023 |
[5] | 魏勤, 李瑛娇, 娄平, 严俊伟, 胡辑伟. 基于边云协同的人脸识别方法研究 Face Recognition Method Based on Edge-Cloud Collaboration 计算机科学, 2022, 49(5): 71-77. https://doi.org/10.11896/jsjkx.210300222 |
[6] | 何嘉玉, 黄宏博, 张红艳, 孙牧野, 刘亚辉, 周哲海. 基于深度学习的单幅图像三维人脸重建研究综述 Review of 3D Face Reconstruction Based on Single Image 计算机科学, 2022, 49(2): 40-50. https://doi.org/10.11896/jsjkx.210500215 |
[7] | 陈长伟, 周晓峰. 快速局部协同表示分类器及其在人脸识别中的应用 Fast Local Collaborative Representation Based Classifier and Its Applications in Face Recognition 计算机科学, 2021, 48(9): 208-215. https://doi.org/10.11896/jsjkx.200800155 |
[8] | 温荷, 罗频捷. 基于改进脉冲耦合神经网络的动态人脸识别 Dynamic Face Recognition Based on Improved Pulse Coupled Neural Network 计算机科学, 2021, 48(6A): 85-88. https://doi.org/10.11896/jsjkx.200600172 |
[9] | 白子轶, 毛懿荣, 王瑞平. 视频人脸识别进展综述 Survey on Video-based Face Recognition 计算机科学, 2021, 48(3): 50-59. https://doi.org/10.11896/jsjkx.210100210 |
[10] | 杨章静, 王文博, 黄璞, 张凡龙, 王昕. 基于局部加权表示的线性回归分类器及人脸识别 Local Weighted Representation Based Linear Regression Classifier and Face Recognition 计算机科学, 2021, 48(11A): 351-359. https://doi.org/10.11896/jsjkx.210100173 |
[11] | 宋一言, 唐东林, 吴续龙, 周立, 秦北轩. 改进穿线法与HOG+SVM结合的数码管图像读数研究 Study on Digital Tube Image Reading Combining Improved Threading Method with HOG+SVM Method 计算机科学, 2021, 48(11A): 396-399. https://doi.org/10.11896/jsjkx.210100123 |
[12] | 栾晓, 李晓双. 基于多特征融合的人脸活体检测算法 Face Anti-spoofing Algorithm Based on Multi-feature Fusion 计算机科学, 2021, 48(11A): 409-415. https://doi.org/10.11896/jsjkx.210100181 |
[13] | 陆要要, 袁家斌, 何珊, 王天星. 基于超分辨率重建的低质量视频人脸识别方法 Low-quality Video Face Recognition Method Based on Super-resolution Reconstruction 计算机科学, 2021, 48(11A): 295-302. https://doi.org/10.11896/jsjkx.201200159 |
[14] | 吴庆洪, 高晓东. 稀疏表示和支持向量机相融合的非理想环境人脸识别 Face Recognition in Non-ideal Environment Based on Sparse Representation and Support Vector Machine 计算机科学, 2020, 47(6): 121-125. https://doi.org/10.11896/jsjkx.190500058 |
[15] | 李新豆,高陈强,周风顺,韩慧,汤林. 基于图像扩散速度模型和纹理信息的人脸活体检测 Face Liveness Detection Based on Image Diffusion Speed Model and Texture Information 计算机科学, 2020, 47(2): 112-117. https://doi.org/10.11896/jsjkx.181202339 |
|