Skip to main content
SpringerLink
Log in

A novel face recognition method based on IWLD and IWBC

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This paper presents a face recognition method using improved Weber local descriptor (IWLD) and improved Weber binary coding method. Compared to the existing Weber local descriptor, the proposed IWLD represent local patterns more effectively and accurately by introducing novel Weber magnitude and orientation components. In order to extract more discriminative and robust feature for face recognition, the IWBC is proposed to encode the cues embedded in IWLD. Moreover, to reduce the dimension of feature extracted by IWBC and enhance its discriminative ability, the block-based Fishers linear discriminant (BFLD) is employed to learn a projection matrix from the training set. Experimental results on three (AR, FERET and PolyU-NIR) challenging databases demonstrate the effectiveness and robustness of our proposed method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Abbreviations

IWLD:

improved Weber local descriptor

WLD:

Weber local descriptor

IWBC:

improved Weber binary code

WBC:

Weber binary coding

FR:

face recognition

BFLD:

block-based Fishers linear discriminant

LSF:

local statistical feature

LBP:

local binary pattern

LBMP :

local binary magnitude pattern

LXOP :

local XOR (exclusive or) orientation pattern

LSF-based FR:

local statistical feature based face recognition

LXP:

local XOR (exclusive or) pattern

IDLS:

image decomposition method based on local structure

LDA:

linear discriminant analysis

References

  1. Ahonen T, Hadid A, Pietikäinen M (2004) Face recognition with local binary patterns. In Computer vision-eccv 2004. Springer, Berlin Heidelberg, pp 469–481

    Book  MATH  Google Scholar 

  2. Bartlett MS, Movellan JR, Sejnowski TJ (2002) Face recognition by independent component analysis. IEEE Trans Neural Netw 13(6):1450–1464

    Article  Google Scholar 

  3. Belhumeur PN, Hespanha JP, Kriegman D (1997) Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  4. Chen J, Shan S, He C, Zhao G, Pietikainen M, Chen X, Gao W (2010) WLD: a robust local image descriptor. IEEE Trans Pattern Anal Mach Intell 32(9):1705–1720

    Article  Google Scholar 

  5. Huang GB, Lee H, Learned-Miller E (2012) Learning hierarchical representations for face verification with convolutional deep belief networks. In Computer Vision and Pattern Recognition (CVPR), 2012 I.E. Conference on, pp. 2518–2525

  6. He X, Yan S, Hu Y, Niyogi P, Zhang HJ (2005) Face recognition using Laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340

    Article  Google Scholar 

  7. He X, Cai D, Yan S, Zhang HJ (2005). Neighborhood preserving embedding. In Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on, 2:1208–1213

  8. Jain AK (1989) Fundamentals of digital signal processing. Prentice-Hall, Englewood Cliffs

    MATH  Google Scholar 

  9. Lei Z, Liao S, Pietikainen M, Li SZ (2011) Face recognition by exploring information jointly in space, scale and orientation. IEEE Trans Image Process 20(1):247–256

    Article  MathSciNet  Google Scholar 

  10. Liu C, Wechsler H (2002) Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans Image Process 11(4):467–476

    Article  Google Scholar 

  11. Li S, Gong D, Yuan Y (2013) Face recognition using Weber local descriptors. Neurocomputing 122:272–283

    Article  Google Scholar 

  12. Mika S, Ratsch G, Weston J, Scholkopf B, Mller KR (1999) Fisher discriminant analysis with kernels, in Proc. Neural Netw. Signal Process 41–48

  13. Martinez AM, Benavente R (1998) The AR face database. CVC Tech. Rep. #24, http://www.cat.uab.cat/Public/Publications/1998/MaB1998/CVCReport24.pdf

  14. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  MATH  Google Scholar 

  15. 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):1090–1104

    Article  Google Scholar 

  16. Phillips PJ, Wechsler H, Huang J, Rauss PJ (1998) The FERET database and evaluation procedure for face-recognition algorithms. Image Vis Comput 16(5):295–306

    Article  Google Scholar 

  17. Qian J, Yang J, Xu Y (2013) Local structure-based image decomposition for feature extraction with applications to face recognition. IEEE Trans Image Process 22(9):3591–3603

    Article  Google Scholar 

  18. Riesenhuber M, Poggio T (1999) Hierarchical models of object recognition in cortex. Nat Neurosci 2(11):1019–1025

    Article  Google Scholar 

  19. Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326

    Article  Google Scholar 

  20. Scholkopf B, Smola A, Mller KR (1999) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10:1299–1319

    Article  Google Scholar 

  21. Siagian C, Itti L (2007) Rapid biologically-inspired scene classification using features shared with visual attention. IEEE Trans Pattern Anal Mach Intell 29(2):300–312

    Article  Google Scholar 

  22. Sun Y, Wang X, Tang X (2013) Deep convolutional network cascade for facial point detection. In Computer Vision and Pattern Recognition (CVPR), 2013 I.E. Conference on, pp. 3476–3483

  23. Tan X, Triggs B (2007) Enhanced local texture feature sets for face recognition under difficult lighting conditions. In Analysis and Modeling of Faces and Gestures, pp. 168–182

  24. Tan X, Triggs B (2007) Fusing Gabor and LBP feature sets for kernel-based face recognition. In Analysis and Modeling of Faces and Gestures, pp. 235–249

  25. Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1650

    Article  MathSciNet  Google Scholar 

  26. Tenenbaum JB, De Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323

    Article  Google Scholar 

  27. Turk MA, Pentland AP (1991) Face recognition using eigenfaces. In Computer Vision and Pattern Recognition, 1991. Proceedings CVPR’91., IEEE Computer Society Conference on, pp. 586–591

  28. Vu NS, Caplier A (2012) Enhanced patterns of oriented edge magnitudes for face recognition and image matching. IEEE Trans Image Process 21(3):1352–1365

    Article  MathSciNet  Google Scholar 

  29. Wang X, Tang X (2004) A unified framework for subspace face recognition. IEEE Trans Pattern Anal Mach Intell 26(9):1222–1228

    Article  Google Scholar 

  30. Wang B, Li W, Yang W, Liao Q (2011) Illumination normalization based on Weber’s law with application to face recognition. IEEE Signal Process Lett 18(8):462–465

    Article  Google Scholar 

  31. Wang B, Li W, Li Z, Liao Q (2012) Patterns of weber magnitude and orientation for face recognition. In Image Processing (ICIP), 2012 19th IEEE International Conference on, pp. 1441–1444

  32. Xie S, Shan S, Chen X, Chen J (2010) Fusing local patterns of gabor magnitude and phase for face recognition. IEEE Trans Image Process 19(5):1349–1361

    Article  MathSciNet  Google Scholar 

  33. Yan S, Xu D, Zhang B, 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):40–51

    Article  Google Scholar 

  34. Yang M, Zhang L, Zhang L, Zhang D (2010) Monogenic Binary Pattern (MBP): A Novel Feature Extraction and Representation Model for Face Recognition. In ICPR 2010, pp. 2680–2683

  35. Yang M, Zhang L, Shiu SK, Zhang D (2012) Monogenic binary coding: an efficient local feature extraction approach to face recognition. IEEE Trans Inf Forensics Secur 7(6):1738–1751

    Article  Google Scholar 

  36. Zhang W, Shan S, Gao W, Chen X, Zhang H (2005) Local gabor binary pattern histogram sequence (lgbphs): A novel non-statistical model for face representation and recognition. In Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on, 1:786–791

  37. Zhang B, Shan S, Chen X, Gao W (2007) Histogram of gabor phase patterns (hgpp): a novel object representation approach for face recognition. IEEE Trans Image Process 16(1):57–68

    Article  MathSciNet  Google Scholar 

  38. Zhang W, Shan S, Qing L, Chen X, Gao W (2009) Are Gabor phases really useless for face recognition? Pattern Anal Applic 12(3):301–307

    Article  MathSciNet  Google Scholar 

  39. Zhao G, Pietikainen M (2007) Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Trans Pattern Anal Mach Intell 29(6):915–928

    Article  Google Scholar 

  40. Zhang T, Tang YY, Fang B, Shang Z, Liu X (2009) Face recognition under varying illumination using gradientfaces. IEEE Trans Image Process 18(11):2599–2606

    Article  MathSciNet  Google Scholar 

  41. Zhang B, Zhang L, Zhang D, Shen L (2010) Directional binary code with application to PolyU near-infrared face database. Pattern Recogn Lett 31(14):2337–2344

    Article  Google Scholar 

  42. Zou J, Ji Q, Nagy G (2007) A comparative study of local matching approach for face recognition. IEEE Trans Image Process 16(10):2617–2628

    Article  MathSciNet  Google Scholar 

  43. Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv (CSUR) 35(4):399–458

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by National Instrument Development Special Program of China under the grants 2013YQ03065101, 2013YQ03065105, Ministry of Science and Technology of China under National Basic Research Project under the grants 2010CB731803, and by National Natural Science Foundation of China under the grants 61221003, 61290322, 61174127, 61273181, 60934003, 61290322 and U1405251, the Program of New Century Talents in University of China under the grant NCET-13-0358, the Science and Technology Commission of Shanghai Municipal, China under the grant 13QA1401900, Postdoctoral Science Foundation of China under the grants 2014M551406.

Author information

Authors and Affiliations

  1. Department of Automation, Shanghai Jiao Tong University, Shanghai, China

    Bao-Qing Yang, Tao Zhang, Chao-Chen Gu, Kai-Jie Wu & Xin-Ping Guan

  2. Key Laboratory of System Controal and Information Processing, Ministry of Education of China, 800 Dongchuan Road, Shanghai, People’s Republic of China

    Bao-Qing Yang, Tao Zhang, Chao-Chen Gu, Kai-Jie Wu & Xin-Ping Guan

Authors
  1. Bao-Qing Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chao-Chen Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kai-Jie Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xin-Ping Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chao-Chen Gu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, BQ., Zhang, T., Gu, CC. et al. A novel face recognition method based on IWLD and IWBC. Multimed Tools Appl 75, 6979–7002 (2016). https://doi.org/10.1007/s11042-015-2623-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-015-2623-4

Keywords

Search

Navigation