Soft Computing

, Volume 23, Issue 16, pp 7015–7028 | Cite as

Feature extraction based on graph discriminant embedding and its applications to face recognition

  • Pu HuangEmail author
  • Tao Li
  • Guangwei Gao
  • Geng Yang
Methodologies and Application


Graph embedding-based learning methods have been widely employed to reduce the dimensionality of high-dimensional data, while how to construct adjacency graphs to discover the essential structure of the data is the key problem in these methods. In this paper, we present a novel algorithm called graph discriminant embedding (GDE) for feature extraction and recognition. GDE combines local information and label information of data points to construct two neighbor graphs, which help to pull the same-class samples nearer and nearer and repel the not-same-class samples farther and farther when they are projected onto a feature subspace. Significantly differing from most of the other graph embedding methods, GDE does not only emphasize the importance of the nearby points but also enhance the importance of the distant points which may have potential advantages for classification. Experimental results on the AR, CMU PIE and FERET face databases demonstrate the effectiveness of the proposed algorithm.


Manifold learning Feature extraction Face recognition Graph construction Marginal Fisher analysis 



This work is supported by the National Natural Science Foundation of China (Grant Nos. 61503195 and 61502245), the China Postdoctoral Science Foundation (Grant No. 2016M600433), Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) (No. MJUKF201717) and Open Fund Project of Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education (Nanjing University of Science and Technology) (No. JYB201709).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Belkin M, Niyogi P (2003) Laplacian Eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396zbMATHCrossRefGoogle Scholar
  2. Chen Y, Li ZZ, Jin Z (2013) Feature extraction based on maximum margin nearest subspace margin criterion. Neural Process Lett 37(3):355–375CrossRefGoogle Scholar
  3. Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27zbMATHCrossRefGoogle Scholar
  4. Gao QX, Zhang HJ, Liu JJ (2012) Two-dimensional margin, similarity and variation embedding. Neurocomputing 86:179–183CrossRefGoogle Scholar
  5. Gu ZH, Yang J (2013) Sparse margin based discriminant analysis for feature extraction. Neural Comput Appl 23(6):1523–1529CrossRefGoogle Scholar
  6. He X, Yan S, Hu Y, Niyogi P, Zhang H (2005) Face recognition using laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340CrossRefGoogle Scholar
  7. Huang P, Chen CK (2009) Enhanced marginal Fisher analysis for face recognition. In: Proceedings of 2009 international conference on artificial intelligence and computational intelligence, pp 403–407Google Scholar
  8. Huang P, Tang ZM, Chen CK, Cheng XT (2011) Nearest-neighbor classifier motivated marginal discriminant projections for face recognition. Front Comput Sci China 5(4):419–428MathSciNetzbMATHCrossRefGoogle Scholar
  9. Huang P, Tang ZM, Yang ZJ, Shi J (2013) Feature extraction using graph discriminant embedding. In: Proceedings of the 2013 6th international congress on image and signal processing (CISP2013), Hangzhou, China, pp 432–436Google Scholar
  10. Huang P, Chen CK, Tang ZM, Yang ZJ (2014a) Discriminant similarity and variance preserving projection for feature extraction. Neurocomputing 139(9):180–188CrossRefGoogle Scholar
  11. Huang P, Tang ZM, Chen CK, Yang ZJ (2014b) Local maximal margin discriminant embedding for face recognition. J Vis Commun Image Represent 25(2):296–305CrossRefGoogle Scholar
  12. Lee CP, Lin CJ (2014) Large-scale linear rank SVM. Neural Comput 26(4):781–817MathSciNetzbMATHCrossRefGoogle Scholar
  13. Li B, Wang C, Huang DS (2009) Supervised feature extraction based on orthogonal discriminant projection. Neurocomputing 73:191–196CrossRefGoogle Scholar
  14. Ma Z, Zhang D, Liu S, Song J, Hou Y (2016) A novel compressive sensing method based on SVD sparse random measurement matrix in wireless sensor network. Eng Comput 33(8):2448–2462CrossRefGoogle Scholar
  15. Ma Z, Zhang DG, Chen J, Hou Y (2017) Shadow detection of moving objects based on multisource information in internet of things. J Exp Theor Artif Intell 29(3):649–661CrossRefGoogle Scholar
  16. Martinez AM, Benavente R (1998) The AR face database. CVC Technical Report #24, JuneGoogle Scholar
  17. Martinez AM, Kak AC (2001) PCA versus LDA. IEEE Trans Pattern Anal Mach Intell 23(2):228–233CrossRefGoogle Scholar
  18. Naseem I, Togneri R, Bennamoun M (2011) Linear regression for face recognition. IEEE Trans Pattern Anal Mach Intell 32(11):2106–2112CrossRefGoogle Scholar
  19. Phillips PJ, Moon H, Rizvi SA, Rauss PJ (2000) The FERET evaluation methodology for face recognition algorithms. IEEE Trans Pattern Anal Mach Intell 22(10):1090–1104CrossRefGoogle Scholar
  20. Raudys SJ, Jain AK (1991) Small sample size effects in statistical pattern recognition: recommendations for practitioners. IEEE Trans Pattern Anal Mach Intell 13(3):252–264CrossRefGoogle Scholar
  21. Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326CrossRefGoogle Scholar
  22. Shi J, Jiang ZG, Feng H (2014) Adaptive graph embedding discriminant projections. Neural Process Lett 40(3):211–226CrossRefGoogle Scholar
  23. Sim T, Baker S, Bsat M (2003) The CMU pose, illuminlation, and expression database. IEEE Trans Pattern Anal Mach Intell 25(12):1615–1618CrossRefGoogle Scholar
  24. Sugiyama M (2007) Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis. J Mach Learn Res 8(1):1027–1061zbMATHGoogle Scholar
  25. Tenenbaum JB, De SV, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323CrossRefGoogle Scholar
  26. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86CrossRefGoogle Scholar
  27. Wright J, Yang AY, Ganesh A, Sastry S, Ma Y (2008) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227CrossRefGoogle Scholar
  28. Yan SC, Xu D, Zhang BY, Zhang HJ, Yang Q, Lin S (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40–51CrossRefGoogle Scholar
  29. Yu WW, Teng XL, Liu CQ (2006) Face recognition using discriminant locality preserving projections. Image Vis Comput 24(3):239–248CrossRefGoogle Scholar
  30. Zhang DG (2012) A new approach and system for attentive mobile learning based on seamless migration. Appl Intell 36(1):75–89CrossRefGoogle Scholar
  31. Zhang D, Liang Y (2013) A kind of novel method of service-aware computing for uncertain mobile applications. Math Comput Model 57(3–4):344–356CrossRefGoogle Scholar
  32. Zhang DG, Zhang XD (2012) Design and implementation of embedded un-interruptible power supply system (EUPSS) for web-based mobile application. Enterpr Inf Syst 6(4):473–489CrossRefGoogle Scholar
  33. Zhang DG, Kang XJ, Wang JH (2012a) A novel image de-noising method based on spherical coordinates system. EURASIP J Adv Signal Process 2012(110):1–10CrossRefGoogle Scholar
  34. Zhang DG, Zhu Y, Zhao C, Dai W (2012b) A new constructing approach for a weighted topology of wireless sensor networks based on local-world theory for the internet of things (IOT). Comput Math Appl 64(5):1044–1055zbMATHCrossRefGoogle Scholar
  35. Zhang DG, Zhao CP, Liang YP, Liu ZJ (2012c) A new medium access control protocol based on perceived data reliability and spatial correlation in wireless sensor network. Comput Electr Eng 38(3):694–702CrossRefGoogle Scholar
  36. Zhang DG, Li G, Zheng K, Ming X, Pan ZH (2014a) An energy-balanced routing method based on forward-aware factor for Wireless Sensor Network. IEEE Trans Ind Inf 10(1):766–773CrossRefGoogle Scholar
  37. Zhang D, Wang X, Song X, Zhao D (2014b) A novel approach to mapped correlation of ID for RFID anti-collision. IEEE Trans Serv Comput 7(4):741–748CrossRefGoogle Scholar
  38. Zhang DG, Wang X, Song XD (2015a) New medical image fusion approach with coding based on SCD in wireless sensor network. J Electr Eng Technol 10(6):2384–2392CrossRefGoogle Scholar
  39. Zhang DG, Song XD, Wang X (2015b) Extended AODV routing method based on distributed minimum transmission (DMT) for WSN. Int J Electron Commun 69(1):371–381CrossRefGoogle Scholar
  40. Zhang DG, Zheng K, Zhang T, Wang X (2015c) A novel multicast routing method with minimum transmission for WSN of cloud computing service. Soft Comput 19(7):1817–1827CrossRefGoogle Scholar
  41. Zhang DG, Wang X, Song XD, Zhang T, Zhu YN (2015d) New clustering routing method based on PECE for WSN. EURASIP J Wirel Commun Netw 2015(162):1–13Google Scholar
  42. Zhang D, Song X, Wang X, Li K, Li W, Ma Z (2015e) New agent-based proactive migration method and system for big data environment (BDE). Eng Comput 32(8):2443–2466CrossRefGoogle Scholar
  43. Zhang DG, Li WB, Liu S, Zhang XD (2016a) Novel fusion computing method for bio-medical image of WSN based on spherical coordinate. J VibroEng 18(1):522–538Google Scholar
  44. Zhang DG, Zheng K, Zhao DX, Song XD, Wang X (2016b) Novel quick start (QS) method for optimization of TCP. Wirel Netw 22(1):211–222CrossRefGoogle Scholar
  45. Zhang DG, Liu S, Zhang T, Liang Z (2017a) Novel unequal clustering routing protocol considering energy balancing based on network partition and distance for mobile education. J Netw Comput Appl 88(15):1–9CrossRefGoogle Scholar
  46. Zhang DG, Niu HL, Liu S (2017b) Novel PEECR-based clustering routing approach. Soft Comput 21(24):7313–7323CrossRefGoogle Scholar
  47. Zhang DG, Zhou S, Tang YM (2017c) A low duty cycle efficient MAC protocol based on self-adaption and predictive strategy. Mobile Netw Appl. CrossRefGoogle Scholar
  48. Zhang DG, Niu HL, Liu S, Ming XC (2017d) Novel positioning service computing method for WSN. Wirel Pers Commun 92(4):1747–1769CrossRefGoogle Scholar
  49. Zhao CR, Lai ZH, Sui Y, Chen Y (2008) Local maximal marginal embedding with application to face recognition. In: Proceedings of 2008 chinese conference on pattern recognition, pp 1–6Google Scholar
  50. Zhao CR, Lai ZH, Liu CC, Gu XJ, Qian JJ (2012) Fuzzy maximal marginal embedding for feature extraction. Soft Comput 16:77–87CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyNanjing University of Posts and TelecommunicationsNanjingChina
  2. 2.Jiangsu Key Laboratory of Big Data Security and Intelligent ProcessingNanjing University of Posts and TelecommunicationsNanjingChina
  3. 3.Fujian Provincial Key Laboratory of Information Processing and Intelligent ControlMinjiang UniversityFuzhouChina
  4. 4.Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of EducationNanjing University of Science and TechnologyNanjingChina

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