Local descriptor margin projections (LDMP) for face recognition

  • Zhangjing YangEmail author
  • Pu Huang
  • Minghua Wan
  • Fanlong Zhang
  • Guowei Yang
  • Chengshan Qian
  • Jincheng Zhang
  • Zuoyong Li
Original Article


Feature extraction is a key problem in face recognition systems. This paper tackles this problem by combining the strength of image descriptor with dimensionality reduction technology. So, this paper proposes a new efficient face recognition method-local descriptor margin projections (LDMP). Firstly, we propose a novel local descriptor for face image representation. At this step, an effective and simple metric approach named gray value accumulating distance (GAD) is firstly proposed. And then a novel local descriptor based on GAD is presented to capture the local structure information between central pixel and its neighbors effectively. Secondly, we propose a dimensionality reduction algorithm named maximum margin learning projections (MMLP) which can obtain the low-dimensional and discriminative feature. Finally, experimental results on the Yale, Extended Yale B, PIE, AR and LFW face databases show the effectiveness of the proposed method.


Face recognition Local descriptor Central pixel Feature extraction 



This work is supported by the National Natural Science Fund of China (Grant Nos. 61503195, 61462064, 61203243,61402231, 61603192 and 61272077), the Natural Science Fund of Jiangsu Province (Grant No. BK20161580), the University Natural Science Fund of Jiangsu Province of China (Grant No. 15KJB520018, 16KJB520020 and 12KJA63001), the Project Funded by China Postdoctoral Science Foundation (Grant No. 2016M600433), the Project Funded by PAPD and CICAEET, and the Project supported by the Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology) (Grant No. 30916014107).


  1. 1.
    Zhao WY, Chellappa R, Phillips PJ et al (2003) Face recognition: a literature survey. ACM Comput Surv 35(4):399–459CrossRefGoogle Scholar
  2. 2.
    Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86CrossRefGoogle Scholar
  3. 3.
    Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs Fisherfaces:recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720CrossRefGoogle Scholar
  4. 4.
    Yuan C, Sun X, Rui L (2016) Fingerprint liveness detection based on multi-scale LPQ and PCA. China Communications. 13(7):60–65Google Scholar
  5. 5.
    Xia Z, Wang X, Zhang L et al (2016) A Privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing. IEEE Trans Inf Forensics Secur 11(11):1–1. doi:  10.1109/TIFS.2016.2590944 CrossRefGoogle Scholar
  6. 6.
    Yan SC, Xu D, Zhang BY et al (2007) Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40–51CrossRefGoogle Scholar
  7. 7.
    Tenenbaum JB, Silva VD, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323CrossRefGoogle Scholar
  8. 8.
    Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326CrossRefGoogle Scholar
  9. 9.
    He XF, Yan SC, Hu YX et al (2003) Learning a locality preserving subspace for visual recognition. In: Proceedings of the 9th IEEE International Conference on Computer Vision, pp 385–392Google Scholar
  10. 10.
    He XF, Yan SC, Hu YX et al (2005) Face recognition using laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340CrossRefGoogle Scholar
  11. 11.
    Lai ZH, Wong WK, Xu Y et al (2015) Approximate orthogonal sparse embedding for dimensionality reduction. IEEE Trans Neural Netw Learn Syst 27(4):723–735MathSciNetCrossRefGoogle Scholar
  12. 12.
    Yang M, Zhu P, Liu F et al (2015) Joint representation and pattern learning for robust face recognition. Neurocomputing 168:70–80CrossRefGoogle Scholar
  13. 13.
    Huang P, Chen CK, Tang ZM et al (2014) Discriminant similarity and variance preserving projection for feature extraction. Neurocomputing 139:180–188CrossRefGoogle Scholar
  14. 14.
    Lai ZH, Xu Y, Jin Z et al (2014) Human gait recognition via sparse discriminant projection learning. IEEE Trans Circuits Syst Video Technol 24(10):1651–1662CrossRefGoogle Scholar
  15. 15.
    Shi X, Guo Z, Lai ZH (2014) Face recognition by sparse discriminant analysis via joint L2,1-norm minimization. Pattern Recognit 47(7):2447–2453CrossRefGoogle Scholar
  16. 16.
    Huang P, Chen CK, Tang ZM et al (2014) Local maximal marginal discriminant embedding for face recognition. J Visual Commun Image Represent 25(2):296–305CrossRefGoogle Scholar
  17. 17.
    Chen Y, Xu XH (2014) Supervised orthogonal discriminant subspace projects learning for face recognition. Neural Netw 50:33–36CrossRefzbMATHGoogle Scholar
  18. 18.
    Huang P, Chen CK, Tang ZM et al (2014) Feature extraction using local structure preserving discriminant analysis. Neurocomputing 140:104–113CrossRefGoogle Scholar
  19. 19.
    Wan MH, Li M, Yang GW et al (2014) Feature extraction using two-dimensional maximum embedding difference. Inf Sci 274:55–69CrossRefGoogle Scholar
  20. 20.
    Cai D, He XF, Zhou K et al (2007) Locality sensitive discriminant analysis. In: Proceedings of the IEEE International Conference on Artificial Intelligence, pp 708–713Google Scholar
  21. 21.
    Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041CrossRefzbMATHGoogle Scholar
  22. 22.
    Zhang BC, Gao YS, Zhao SQ et al (2010) Local derivative pattern versus local binary pattern:face recognition with high-order local pattern descriptor. IEEE Trans Image Process 19(2):533–544MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Zhang WC, Shan SG, Gao W et al (2005) Local Gabor binary pattern histogram sequence (LGBPHS):a novel non-statistical model for face representation and recognition. In: Proceedings of the 10th IEEE International Conference on Computer Vision, pp 786–791Google Scholar
  24. 24.
    Tan XY, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1650MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    [25] Wolf L, Hassner T, Taigman Y (2011) Effective unconstrained face recognition by combining multiple descriptors and learned background statistics. IEEE Trans Pattern Anal Mach Intell 33(10):1978–1990CrossRefGoogle Scholar
  26. 26.
    Liu CJ, Wechsler H (2002) Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition. IEEE Trans Image Process 11(4):467–476CrossRefGoogle Scholar
  27. 27.
    Pang Y, Yuan Y, Li X (2008) Gabor-based region covariance matrices for face recognition. IEEE Trans Circuits Syst Video Technol 18(7):989–993CrossRefGoogle Scholar
  28. 28.
    Zhang BC, Shan SG, Chen XL et al (2007) Histogram of gabor phase patterns (HGPP): a novel object representation approach for face recognition. IEEE Trans Image Process 16(1):57–68MathSciNetCrossRefGoogle Scholar
  29. 29.
    Lei Z, Liao SC, Pietikainen M et al (2011) Face recognition by exploring information jointly in space, scale and orientation. IEEE Trans Image Process 20(1):247–256MathSciNetCrossRefGoogle Scholar
  30. 30.
    Gai S, Yang GW, Wan MH (2013) Employing quaternion wavelet transform for banknote classification. Neurocompuing 118(8):171–178CrossRefGoogle Scholar
  31. 31.
    Gu B, Sheng VS, Tay KY et al (2014) Incremental support vector learning for ordinal regression[J]. IEEE Trans Neural Netw Learn Syst 26(7):1403–1416MathSciNetCrossRefGoogle Scholar
  32. 32.
    Wen X, Shao L, Xue Y et al (2015) A rapid learning algorithm for vehicle classification. Inf Sci 295:395–406CrossRefGoogle Scholar
  33. 33.
    Gu B, Sheng VS (2016) A robust regularization path algorithm for ν-support vector classification. IEEE Trans Neural Netw Learn Syst. doi: 10.1109/TNNLS.2016.2527796 Google Scholar
  34. 34.
    Qian JJ, Yang J, Gao GW (2013) Discriminative histograms of local dominant orientation (D-HLDO) for biometric image feature extraction. Pattern Recognit 46(10):2724–2739CrossRefGoogle Scholar
  35. 35.
    Nouyed I, Poon B, Amin MA et al (2015) A study on the discriminating characteristics of Gabor phase-face and an improved method for face recognition. Int J Mach Learn Cyber 7(6):1115–1130CrossRefGoogle Scholar
  36. 36.
    Lai ZH, Xu Y, Chen QC et al (2014) Multilinear Sparse Principal Component Analysis. IEEE Trans Neural Netw Learn Syst 25(10):1942–1950CrossRefGoogle Scholar
  37. 37.
    Lai ZH, Wong WK, Xu Y et al (2014) Sparse alignment for robust tensor learning. IEEE Trans Neural Netw Learn Syst 25(10):1779–1792CrossRefGoogle Scholar
  38. 38.
    Seo HJ, Milanfar P (2010) Training-Free, generic object detection using locally adaptive regression kernels. IEEE Trans Pattern Anal Mach Intell 32(9):1688–1704CrossRefGoogle Scholar
  39. 39.
    [39] Seo HJ, Milanfar P (2011) Face verification using the LARK representation. IEEE Trans Inf Forensics Secur 6(4):1275–1286CrossRefGoogle Scholar
  40. 40.
    Liao SC, Zhu XX, Lei Z et al (2007) Learning multi-scale block local binary patterns for face recognition. In: Proceedings of the IEEE International Conference on Biometrics vol 4642, pp 828–837Google Scholar
  41. 41.
    Qian JJ, Yang J, Xu Y (2013) Local structure-based image decomposition for feature extraction with applications to face recognition. IEEE Trans Image Process 22(9):3591–3603CrossRefGoogle Scholar
  42. 42.
    Sugiyama M (2007) Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. J Mach Learn Res 8(5):1027–1061zbMATHGoogle Scholar
  43. 43.
    Lee KC, Ho J, Kriegman DJ (2005) Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans Pattern Anal Mach Intell 27(5):684–698CrossRefGoogle Scholar
  44. 44.
    Gross R, Matthews I, Cohn J et al (2007) The cmu multi-pose, illumination, and expression (multi-pie) face database. Technical report #07–08. Carnegie Mellon University Robotics InstituteGoogle Scholar
  45. 45.
    Martinez AM, Benavente R (1998) The AR face database. CVC Technical Report #24Google Scholar
  46. 46.
    Huang GB, Ramesh M, Berg T et al (2007) Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report# 07–49. University of Massachusetts, AmherstGoogle Scholar
  47. 47.
    Wolf L, Hassner T, Taigman Y (2009) Similarity scores based on background samples. Computer Vision—ACCV 2009. Springer, Berlin, pp 88–97Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Zhangjing Yang
    • 1
    • 2
    • 6
    Email author
  • Pu Huang
    • 3
    • 4
  • Minghua Wan
    • 1
  • Fanlong Zhang
    • 1
  • Guowei Yang
    • 1
  • Chengshan Qian
    • 5
  • Jincheng Zhang
    • 1
  • Zuoyong Li
    • 6
  1. 1.School of TechnologyNanjing Audit UniversityNanjingChina
  2. 2.School of Computer Science and EngineeringSoutheast UniversityNanjingChina
  3. 3.School of Computer Science and TechnologyNanjing University of Posts and TelecommunicationsNanjingChina
  4. 4.Key Laboratory of Image and Video Understanding for Social SafetyNanjing University of Science and TechnologyNanjingChina
  5. 5.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina
  6. 6.Fujian Provincial Key Laboratory of Information Processing and Intelligent ControlFuzhouChina

Personalised recommendations