Skip to main content
SpringerLink
Log in

Multimodal 2d + 3d multi-descriptor tensor for face verification

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

Abstract

In the last few years, there is a growing interest in multilinear subspace learning for dimensionality reduction of multidimensional data. In this paper, we proposed a multimodal 2D + 3D face verification system based on Multilinear Discriminant Analysis MDA integrating Within Class Covariance Normalization WCCN technique. Histograms of local descriptor applied to features extraction from 2D and 3D face images are concatenated and organized as a tensor design. This tensor is then reduced and projected using MDA technique into a lower subspace. WCCN technique is used to reduce the effect of the intra class directions using normalisation transform and to enhance the discrimination power of the MDA. Our experiments were carried out on the three biggest databases: FRGC v2.0, Bosphorus and CASIA 3D under expressions, occlusions and pose variations. Experimental results showed the superiority of the proposed approach in term of verification rate when compared to the state of the art 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

Similar content being viewed by others

References

  1. Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: A literature survey. ACM Comput Surv 35(4):399–458. https://doi.org/10.1145/954339.954342

  2. Drira H, Amor BB, Srivastava A, Daoudi M, Slama R (2013) 3D face recognition under expressions, occlusions, and pose variations. IEEE Trans Pattern Anal Mach Intell 35(9):2270–2283. https://doi.org/10.1109/TPAMI.2013.48

  3. Spreeuwers L (2011) Fast and accurate 3D face recognition. Int J Comput Vis 93(3):389–414. https://doi.org/10.1007/s11263-011-0426-2

    Article  MATH  Google Scholar 

  4. Al-Osaimi F, Bennamoun M, Mian A (2008) An expression deformation approach to non-rigid 3D face recognition. Int J Comput Vis 81(3):302–316. https://doi.org/10.1007/s11263-008-0174-0

    Article  Google Scholar 

  5. Faltemier TC, Bowyer KW, Flynn PJ (2008) A region ensemble for 3-D face recognition. IEEE Trans Inf Forensics Secur 3(1):62–73. https://doi.org/10.1109/tifs.2007.916287

    Article  Google Scholar 

  6. Bagchi P, Bhattacharjee D, Nasipuri M (2014) Robust 3D face recognition in presence of pose and partial occlusions or missing parts. International Journal in Foundations of Computer Science & Technology 4(4):21–35. https://doi.org/10.5121/ijfcst.2014.4402

    Article  Google Scholar 

  7. Ming Y (2014) Rigid-area orthogonal spectral regression for efficient 3D face recognition. Neurocomputing 129:445–457. https://doi.org/10.1016/j.neucom.2013.09.014

    Article  Google Scholar 

  8. Chouchane A, Ouamane A, Boutellaa E, Belahcene M, Bourennane S (2017) 3D face verification across pose based on euler rotation and tensors. Multimed Tools Appl 77(16):20697–20714. https://doi.org/10.1007/s11042-017-5478-z

    Article  Google Scholar 

  9. Queirolo CC, Silva L, Bellon ORP, Segundo MP (2010) 3D face recognition using simulated annealing and the surface interpenetration measure. IEEE Trans Pattern Anal Mach Intell 32(2):206–219. https://doi.org/10.1109/tpami.2009.14

    Article  Google Scholar 

  10. Bowyer KW, Chang K, Flynn P (2006) A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition. Comput Vis Image Underst 101(1):1–15. https://doi.org/10.1016/j.cviu.2005.05.005

    Article  Google Scholar 

  11. Ouamane A, Belahcene M, Benakcha A, Bourennane S, Taleb-Ahmed A (2014) Robust multimodal 2D and 3D face authentication using local feature fusion. SIViP 10(1):129–137. https://doi.org/10.1007/s11760-014-0712-x

    Article  Google Scholar 

  12. Ouamane A, Boutellaa E, Bengherabi M, Taleb-Ahmed A, Hadid A (2017) A novel statistical and multiscale local binary feature for 2D and 3D face verification. Comput Electr Eng 62:68–80. https://doi.org/10.1016/j.compeleceng.2017.01.001

    Article  Google Scholar 

  13. Xu C, Li S, Tan T, Quan L (2009) Automatic 3D face recognition from depth and intensity Gabor features. Pattern Recogn 42(9):1895–1905. https://doi.org/10.1016/j.patcog.2009.01.001

    Article  MATH  Google Scholar 

  14. Bessaoudi M, Belahcene M, Ouamane A, Chouchane A, Bourennane S (2018) Multilinear enhanced fisher discriminant analysis for robust multimodal 2D and 3D face verification. Appl Intell 49(4):1339–1354. https://doi.org/10.1007/s10489-018-1318-8

    Article  Google Scholar 

  15. Weighted Gradient Feature Extraction Based on Multiscale Sub-Blocks for 3D Facial Recognition in Bimodal Images (2018) Information 9 (3). https://doi.org/10.3390/info9030048

  16. Mian AS, Bennamoun M, Owens R (2007) Keypoint detection and local feature matching for textured 3D face recognition. Int J Comput Vis 79(1):1–12. https://doi.org/10.1007/s11263-007-0085-5

    Article  Google Scholar 

  17. Mian AS, Bennamoun M, Owens R (2007) An efficient multimodal 2D-3D hybrid approach to automatic face recognition. IEEE Trans Pattern Anal Mach Intell 29(11):1927–1943. https://doi.org/10.1109/TPAMI.2007.1105

    Article  Google Scholar 

  18. Turk M, Pentland A (1991) Face recognition using eigenfaces. In: Proceedings. IEEE computer society conference on computer vision and pattern recognition, 1991. pp 586,587,588,589,590,591-586,587,588,589,590,591

  19. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720. https://doi.org/10.1109/34.598228

  20. Yang J, Zhang D, Frangi AF, Yang J-Y (2004) Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans Pattern Anal Mach Intell 26(1):131–137. https://doi.org/10.1109/TPAMI.2004.1261097

  21. Li M, Yuan BA (2004) Novel statistical linear discriminant analysis for image matrix: two-dimensional fisherfaces. In: Proceedings 7th International Conference on Signal Processing, 2004. Proceedings. ICSP'04. 2004. IEEE, pp 1419–1422

  22. Lu H, Plataniotis KN, Venetsanopoulos AN (2008) MPCA: Multilinear principal component analysis of tensor objects. IEEE Trans Neural Netw 19(1):18–39. https://doi.org/10.1109/TNN.2007.901277

  23. Yan S, Xu D, Yang Q, Zhang L, Tang X, Zhang HJ (2007) Multilinear discriminant analysis for face recognition. IEEE Trans Image Process 16(1):212–220. https://doi.org/10.1109/tip.2006.884929

    Article  MathSciNet  Google Scholar 

  24. Zhang T, Fang B, Tang YY, Shang Z, Xu B (2010) Generalized discriminant analysis: a matrix exponential approach. IEEE Trans Syst Man Cybern B Cybern 40(1):186–197. https://doi.org/10.1109/TSMCB.2009.2024759

    Article  Google Scholar 

  25. Liu C, Wechsler H (1998) Enhanced fisher linear discriminant models for face recognition. In: Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No. 98EX170). IEEE, pp 1368–1372

  26. Ouamane A, Chouchane A, Boutellaa E, Belahcene M, Bourennane S, Hadid A (2017) Efficient tensor-based 2D+3D face verification. IEEE Trans Inf Forensics Secur 12(11):2751–2762. https://doi.org/10.1109/tifs.2017.2718490

    Article  Google Scholar 

  27. Hatch AO, Kajarekar S, Stolcke A (2006) Within-class covariance normalization for SVM-based speaker recognition. In: Ninth international conference on spoken language processing.

  28. He Y, Liang B, Yang J, Li S, He J (2017) An iterative closest points algorithm for registration of 3D laser scanner point clouds with geometric features. Sensors (Basel) 17(8). https://doi.org/10.3390/s17081862

  29. Ojansivu V, Heikkilä J (2008) Blur insensitive texture classification using local phase quantization. In: International conference on image and signal processing. Springer, pp 236–243

  30. Kannala J, Rahtu E (2012) Bsif: Binarized statistical image features. In: Proceedings of the 21st international conference on pattern recognition (ICPR2012). IEEE, pp 1363–1366

  31. Phillips PJ, Flynn PJ, Scruggs T, Bowyer KW, Chang J, Hoffman K, Marques J, Min J, Worek W (2005) Overview of the face recognition grand challenge. In: IEEE computer society conference on computer vision and pattern recognition (CVPR'05), 2005. IEEE, pp 947–954

  32. Savran A, Alyüz N, Dibeklioğlu H, Çeliktutan O, Gökberk B, Sankur B, Akarun L (2008) Bosphorus database for 3D face analysis. In: European Workshop on Biometrics and Identity Management. Springer, pp 47–56

  33. Xu C, Wang Y, Tan T, Quan L (2004) 3D face recognition based on GH shape variation. In: Chinese Conference on Biometric Recognition. Springer, pp 233–243

  34. Hu H, Shah SAA, Bennamoun M, Molton M 2017-2017 2D and 3D face recognition using convolutional neural network. In: TENCON IEEE region 10 conference. IEEE, pp 133–132

  35. Feng J, Guo Q, Guan Y, Wu M, Zhang X, Ti C (2019) 3D face recognition method based on Deep convolutional neural network. In: Smart Innovations in Communication and Computational Sciences. Advances in Intelligent Systems and Computing. pp 123–130. https://doi.org/10.1007/978-981-10-8971-8_12

  36. Olivetti EC, Ferretti J, Cirrincione G, Nonis F, Tornincasa S, Marcolin F (2020) Deep CNN for 3D Face Recognition. In, Cham. Design Tools and Methods in Industrial Engineering. Springer International Publishing, pp 665–674

  37. Wang Y, Liu J, Tang X (2010) Robust 3D face recognition by local shape difference boosting. IEEE Trans Pattern Anal Mach Intell 32(10):1858–1870. https://doi.org/10.1109/TPAMI.2009.200

  38. Kakadiaris IA, Passalis G, Toderici G, Murtuza MN, Lu Y, Karampatziakis N, Theoharis T (2007) Three-dimensional face recognition in the presence of facial expressions: An annotated deformable model approach. IEEE Trans Pattern Anal Mach Intell 29(4):640–649. https://doi.org/10.1109/TPAMI.2007.1017

  39. Ammar C, Mebarka B, Abdelmalik O, Salah B (2016) Evaluation of histograms local features and dimensionality reduction for 3D face verification. J Inf Process Syst 12(3):468–488. https://doi.org/10.3745/JIPS.02.0037

  40. Bessaoudi M, Belahcene M, Ouamane A, Chouchane A, Bourennane S 2019 A Novel Hybrid Approach for 3D Face Recognition Based on Higher Order Tensor. In, Cham. Advances in Computing Systems and Applications. Springer International Publishing, 215–224

  41. Elaiwat, S., Bennamoun, M., Boussaid, F., & El-Sallam, A. (2015). A Curvelet-based approach for textured 3D face recognition. Pattern Recognition, 48(4), 1235–1246. https://doi.org/10.1016/j.patcog.2014.10.013

Download references

Author information

Authors and Affiliations

  1. University of Biskra, Biskra, Algeria

    Adel Saoud, Abdelmalik Oumane & Abdelkrim Ouafi

  2. University of Valenciennes, Valenciennes, France

    Abdelmalik Taleb-Ahmed

Authors
  1. Adel Saoud
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abdelmalik Oumane
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abdelkrim Ouafi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Abdelmalik Taleb-Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adel Saoud.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saoud, A., Oumane, A., Ouafi, A. et al. Multimodal 2d + 3d multi-descriptor tensor for face verification. Multimed Tools Appl 79, 23071–23092 (2020). https://doi.org/10.1007/s11042-020-09095-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09095-y

Keywords

Search

Navigation