王钰;刘凡;王菲;

• 1:河海大学海岸灾害及防护教育部重点实验室
• 2:河海大学计算机与信息学院

摘要(Abstract)：

单样本人脸识别因其在现实生活中的广泛应用而成为人脸识别领域的热门话题。单张训练样本条件下训练样本的缺少和复杂的类内人脸表情、光照、遮挡变化给单样本人脸识别研究带来困难。传统的基于稀疏表示的人脸识别方法需要大量的训练样本构成过完备的字典,因而在单样本条件下识别效果明显下滑。针对这一问题,提出一种基于有监督自编码器的带变化人脸样本生成方法,在保留身份信息的同时自动生成带变化的人脸图像用于单样本条件下的字典扩充,一定程度上缓解了单样本条件下的欠采样问题,弥补了训练集和测试集间的人脸变化信息差异,使得传统的稀疏表示方法能够适用于单样本人脸识别问题。在公共数据库上的实验结果不仅证明了该方法的有效性,而且对测试集中不同的人脸变化也展现出了较强的鲁棒性。

关键词(KeyWords)： 单样本人脸识别;有监督自编码器;稀疏表示;字典学习

Abstract:

Keywords:

基金项目(Foundation): 江苏省自然科学基金（BK20191298）;; 河海大学海岸灾害及防护教育部重点实验室开放基金（201905）

作者(Author): 王钰;刘凡;王菲;

Email:

DOI:

参考文献(References)：

• [1] WRIGHT J,YANG A Y,GANESH A,et al.Robust face recognition via sparse representation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2008,31(2):210-227.
• [2] YAN X.Single sample face recognition based on sample augments and MSD fusion[C]//Proceedings of 2016IEEE Information Technology,Networking,Electronic and Automation Control Conference,2016:352-355.
• [3] ZHAO Y N,MA Y,JI S W.Face recognition with single training image per person based on wavelet transform and virtual information[C]//Proceedings of First International Conference on Pervasive Computing Signal Processing&Applications,2010.
• [4] ZHANG E,LI Y,ZHANG F.A single training sample face recognition algorithm based on sample extension[C]//Proceedings of 2013 Sixth International Conference on Advanced Computational Intelligence(ICACI),2013:324-327.
• [5]刘江，李明珠，刘国攸，等.基于稀疏重构扩充法的单样本人脸识别算法[J].杭州电子科技大学学报（自然科学版），2019,39(2):52-56.
• [6] ZHU N,CHEN S.Weighted sparse representation based on virtual test samples for face recognition[J].Optik,2017,140:853-859.
• [7] ZHU P,ZHANG L,HU Q,et al.Multi-scale patch based collaborative representation for face recognition with margin distribution optimization[C]//Proceedings of European Conference on Computer Vision,2012:822-835.
• [8] PANG M,CHEUNG Y M,WANG B,et al.Robust heterogeneous discriminative analysis for face recognition with single sample per person[J].Pattern Recognition,2019,89:91-107.
• [9] GAO S,ZHANG Y,JIA K,et al.Single sample face recognition via learning deep supervised autoencoders[J].IEEE Transactions on Information Forensics and Security,2015,10(10):2108-2118.
• [10] OUANAN H,OUANAN M,AKSASSE B.Non-linear dictionary representation of deep features for face recognition from a single sample per person[J].Procedia Computer Science,2018,127:114-122.
• [11] PEI T,ZHANG L,WANG B,et al.Decision pyramid classifier for face recognition under complex variations using single sample per person[J].Pattern Recognition,2017,64:305-313.
• [12] HUANG K K,DAI D Q,REN C X,et al.Learning kernel extended dictionary for face recognition[J].IEEE Transactions on Neural Networks and Learning Systems,2016,28(5):1082-1094.
• [13] YANG M,ZHANG L.Local generic representation for face recognition with single sample per person[C]//Proceedings of Asian Conference on Computer Vision,2014:34-50.
• [14] DENG W,HU J,GUO J.Extended SRC:Undersampled face recognition via intraclass variant dictionary[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,34(9):1864-1870.
• [15]韩旭，谌海云，王溢，等.基于SPCA和HOG的单样本人脸识别算法[J].计算机科学，2019(S1):274-278.
• [16] GAO S,JIA K,ZHUANG L,et al.Neither global nor local:Regularized patch-based representation for single sample per person face recognition[J].International Journal of Computer Vision,2015,111(3):365-383.
• [17] PANG M,CHEUNG Y M,WANG B,et al.Synergistic generic learning for face recognition from a contaminated single sample per person[J].IEEE Transactions on Information Forensics and Security,2019,15:195-209.
• [18] ZHANG L,YANG M,FENG X.Sparse representation or collaborative representation:Which helps face recognition?[C]//Proceedings of 2011 International Conference on Computer Vision,2011:471-478.
• [19] 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 and Machine Intelligence,2001,23(6):643-660.
• [20] MARTINEZ A M.The AR face database:CVC Technical Report24[R].1998.
• [21] LIU F,TANG J,SONG Y,et al.Local structure based multi-phase collaborative representation for face recognition with single sample per person[J].Information Sciences,2016,346:198-215.