Multi-scale multi-block covariance descriptor with feature selection

  • Abdelmalik MoujahidEmail author
  • Fadi Dornaika
Original Article


This paper investigates a compact face texture representation able to cover the most discriminant features of facial images. The compactness is achieved by the proposed Pyramid Multi-Level (PML) covariance texture descriptor and the feature selection process that is applied on the raw extracted features. In fact, we introduce a framework based mainly on two new aspects. Firstly, we consider an extension of the original covariance descriptor that relies on de-noised covariance matrices obtained using texture descriptors such as local binary pattern and quaternionic local ranking binary pattern images. Secondly, we exploit the resulting covariance descriptor using a PML face representation which allows a multi-level multi-scale feature extraction. Experiments conducted on four public face datasets show the efficacy of the proposed face descriptor and the associated selection schemes.


Face texture representation Feature selection Face recognition 


Compliance with ethical standards

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


  1. 1.
    Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1 (NIPS'12), vol 1. Curran Associates Inc., USA, pp 1097–1105Google Scholar
  2. 2.
    Memon I, Chen L, Majid A, Lv M, Hussain I, Chen G (2015) Travel recommendation using geo-tagged photos in social media for tourist. Wirel Pers Commun 80(4):13471362CrossRefGoogle Scholar
  3. 3.
    Zhou Z, Feng J (2017) Deep forest: towards an alternative to deep neural networks. arXiv:1702.08835v2
  4. 4.
    Lou Z, Alnajar F, Alvarez JM, Hu N, Gevers T (2018) Expression-invariant age estimation using structured learning. IEEE Trans Pattern Anal Mach Intell 40(2):365–375CrossRefGoogle Scholar
  5. 5.
    Zhu Q, Yuan N, Guan D, Xu N, Li H (2018) An alternative to face image representation and classification. Int J Mach Learn Cybern. Google Scholar
  6. 6.
    Memon MH, Li J, Memon I, Shaikh RA, Mangi FA (2015) Efficient object identification and multiple regions of interest using CBIR based on relative locations and matching regions. In: 12th International computer conference on wavelet active media technology and information processing (ICCWAMTIP), pp 247–250Google Scholar
  7. 7.
    Memon MH, Li J, Memon I, Arain QA (2017) GEO matching regions: multiple regions of interests using content based image retrieval based on relative locations. Multimed Tools Appl 76(14):15377–15411CrossRefGoogle Scholar
  8. 8.
    Pietikäinen M, Ojala T, Xu Z (2000) Rotation-invariant texture classification using feature distributions. Pattern Recognit 33(1):43–52CrossRefGoogle Scholar
  9. 9.
    Nanni L, Brahnam S, Lumini A (2012) A simple method for improving local binary patterns by considering non-uniform patterns. Pattern Recognit 45(10):3844–3852CrossRefGoogle Scholar
  10. 10.
    Yang B, Chen S (2013) A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image. Neurocomputing 120:365–379 (Image Feature Detection and Description) CrossRefGoogle Scholar
  11. 11.
    Girish GN, Shrinivasa Naika CL, Das PK (2014) Face recognition using MB-LBP and PCA: a comparative study. In: International conference on computer communication and informatics, pp 1–6Google Scholar
  12. 12.
    Takala V, Ahonen T, Pietikainen M (2005) Block-based methods for image retrieval using local binary patterns. In: Image analysis, SCIA, volume LNCS, 3540Google Scholar
  13. 13.
    Ojala T, Pietikäinen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987CrossRefzbMATHGoogle Scholar
  14. 14.
    Zhou H, Wang R, Wang C (2008) A novel extended local-binary-pattern operator for texture analysis. Inf Sci 178(22):4314–4325CrossRefzbMATHGoogle Scholar
  15. 15.
    Nguyen DT, Cho SR, Park KR (2014) Human age estimation based on multi-level local binary pattern and regression method. In: Park J, Pan Y, Kim CS, Yang Y (eds) Future information technology. Lecture notes in electrical engineering, vol 309. Springer, Berlin, HeidelbergGoogle Scholar
  16. 16.
    Bekhouche S, Ouafi A, Benlamoudi A, Taleb-Ahmed A, Hadid A (2015) Automatic age estimation and gender classification in the wild. In: Proceeding of the international conference on automatic control, telecommunications and signals ICATS’15Google Scholar
  17. 17.
    Wang W, Chen W, Xu D (2011) Pyramid-based multi-scale lbp features for face recognition. In: International conference on multimedia and signal processing (CMSP), vol 1, pp 151–155Google Scholar
  18. 18.
    Bekhouche SE, Ouafi A, Dornaika F, Taleb-Ahmed A, Hadid A (2017) Pyramid multi-level features for facial demographic estimation. Expert Syst Appl 80(Supplement C):297–310CrossRefGoogle Scholar
  19. 19.
    Lan R, Zhou Y, Tang YY (2016) Quaternionic local ranking binary pattern: a local descriptor of color images. IEEE Trans Image Process 25(2):566–579MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Kannala J, Rahtu E (2012) BSIF: binarized statistical image features. In: 21st International conference on pattern recognition (ICPR), pp 1363–1366Google Scholar
  21. 21.
    Dornaika F, Moujahid A, El Merabet Y, Ruichek Y (2016) Building detection from orthophotos using a machine learning approach: an empirical study on image segmentation and descriptors. Expert Syst Appl 58:130–142CrossRefGoogle Scholar
  22. 22.
    Moujahid A, Dornaika F (2018) A pyramid multi-level face descriptor: application to kinship verification. Multimed Tools Appl. Google Scholar
  23. 23.
    Huang SH (2015) Supervised feature selection: a tutorial. Artif Intell Res 4(2):22–37CrossRefGoogle Scholar
  24. 24.
    Peng Z, Gurram P, Kwon H, Yin W (2015) Sparse kernel learning-based feature selection for anomaly detection. IEEE Trans Aerosp Electron Syst 51(3):1698–1716CrossRefGoogle Scholar
  25. 25.
    Koller D, Sahami M (1996) Toward optimal feature selection. In: Saitta L (ed) Proceedings of the thirteenth international conference on international conference on machine learning (ICML’96). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 284–292Google Scholar
  26. 26.
    Robnik-Sikonja M, Kononenko I (2003) Theoretical and empirical analysis of ReliefF and RReliefF. Mach Learn 53:23CrossRefzbMATHGoogle Scholar
  27. 27.
    He X, Cai D, Niyogi P (2005) Laplacian score for feature selection. In: Weiss Y, Schlkopf B, Platt JC (eds) Proceedings of the 18th international conference on neural information processing systems (NIPS’05). MIT Press, Cambridge, MA, USA, pp 507–514Google Scholar
  28. 28.
    Gu Q, Li Z, Han J (2011) Generalized Fisher score for feature selection. In: Cozman F, Pfeffer A (eds) Proceedings of the twenty-seventh conference on uncertainty in artificial intelligence, (UAI’11). AUAI Press, Arlington, Virginia, United States, pp 266–273Google Scholar
  29. 29.
    Kumar V, Minz S (2014) A survey on feature selection methods. Smart Comput Rev 4(3):216–2229CrossRefGoogle Scholar
  30. 30.
    Chandrashekar G, Sahi F (2014) A survey on feature selection methods. Comput Electr Eng 40:16–28CrossRefGoogle Scholar
  31. 31.
    Davarpanah SH, Khalid F, Nurliyana AL, Golchin M (2016) A texture descriptor: background local binary pattern (bglbp). Multimed Tools Appl 75(11):6549–6568CrossRefGoogle Scholar
  32. 32.
    Bianconi F, Bello R, Napoletano P, Di Maria F (2017) Improved opponent colour local binary patterns for colour texture classification. In: Workshop computational color imaging workshop, CCIWGoogle Scholar
  33. 33.
    Silva C, Bouwmans T, Frélicot C (2015) An extended center-symmetric local binary pattern for background modeling and subtraction in videos. In: Proceedings of the 10th international conference on computer vision theory and applications, volume 1: VISAPP, (VISIGRAPP 2015), pp 395–402Google Scholar
  34. 34.
    Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19:1635–1650MathSciNetCrossRefzbMATHGoogle Scholar
  35. 35.
    Ahonen T, Hadid A, Pietikinen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041CrossRefGoogle Scholar
  36. 36.
    Mäenpää T, Pietikainen M (2004) Classification with color and texture: jointly or separately? Pattern Recognit 37(8):1629–1640CrossRefGoogle Scholar
  37. 37.
    Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE Computer Society conference on computer vision and pattern recognition, CVPR 2005, vol 1, pp 886–893. IEEEGoogle Scholar
  38. 38.
    Tuzel O, Porikli F, Meer P (2006) A fast descriptor for detection and classification. In: European conference on computer vision, pp 589–600Google Scholar
  39. 39.
    Jushan B, Shuzhong S (2011) Estimating high dimensional covariance matrices and its applications. Ann Econ Finance 12(2):199–215Google Scholar
  40. 40.
    Laloux L, Cizeau P, Bouchaud JP, Potters M (1999) Noise dressing of financial correlation matrices. Phys Rev Lett 83:1467CrossRefGoogle Scholar
  41. 41.
    Laloux L, Cizeau P, Bouchaud JP, Potters M (2000) Random matrix theory and financial correlations. Int J Theor Appl Finance 3:391–397CrossRefzbMATHGoogle Scholar
  42. 42.
    Szeliski R (2011) Computer vision: algorithms and applications. In: Gries D, Schneider FB (eds) Computer vision. Springer, London, p 812CrossRefGoogle Scholar
  43. 43.
    Guan D, Yuan W, Lee Y-K, Najeebullah K, Rasel MK (2014) A review of ensemble learning based feature selection. IETE Tech Rev 31(3):190–198CrossRefGoogle Scholar
  44. 44.
    The Georgia Tech face database (1999).
  45. 45.
    Belhumeur PN, Hespanha JP, Kriegman DJ (1996) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. In: Bernard B, Roberto C (eds) Computer vision ECCV ’96, volume 1064 of lecture notes in computer science. Springer, Berlin, pp 43–58Google Scholar
  46. 46.
    Samaria FS, Harter AC (1994) Parameterisation of a stochastic model for human face identification. In: Proceedings of the second IEEE workshop on applications of computer vision, pp 138–142Google Scholar
  47. 47.
    The FEI face database (2006).
  48. 48.
    Zhang L, Yang M, Feng X (2011) Sparse representation or collaborative representation: which helps face recognition? In: International conference on computer vision, Barcelona, pp 471–478.
  49. 49.
    Yang A, Sastry S, Ganesh A, Ma Y (2010) Fast \(\ell _1\)-minimization algorithms and an application in robust face recognition: a review. In: IEEE international conference on image processingGoogle Scholar
  50. 50.
    Fan Z, Ni M, Zhu Q, Sun C, Kang L (2015) L0-norm sparse representation based on modified genetic algorithm for face recognition. J Vis Commun Image Represent 28:15–20CrossRefGoogle Scholar
  51. 51.
    Beck A, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2(1):183–202MathSciNetCrossRefzbMATHGoogle Scholar
  52. 52.
    Liu Z, Pu J, Huang T, Qiu Y (2013) A novel classification method for palmprint recognition based on reconstruction error and normalized distance. Appl Intell 39:407414Google Scholar
  53. 53.
    Yang Z, Jia D, Ioannidis S, Mi N, Sheng B (2018) Intermediate data caching optimization for multi-stage and parallel big data frameworks. In: IEEE 11th international conference on cloud computing (CLOUD)Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.University of the Basque Country (UPV/EHU)San SebastiánSpain

Personalised recommendations