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3D face verification across pose based on euler rotation and tensors

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Abstract

In this paper, we propose a new approach for 3D face verification based on tensor representation. Face challenges, such as illumination, expression and pose, are modeled as a multilinear algebra problem where facial images are represented as high order tensors. Particularly, to account for head pose variations, several pose scans are generated from a single depth image using Euler transformation. Multi-bloc local phase quantization (MB-LPQ) histogram features are extracted from depth face images and arranged as a third order tensor. The dimensionality of the tensor is reduced based on the higher-order singular value decomposition (HOSVD). HOSVD projects the input tensor in a new subspace in which the dimension of each tensor mode is reduced. To discriminate faces of different persons, we utilize the Enhanced Fisher Model (EFM). Experimental evaluations on CASIA-3D database, which contains large head pose variations, demonstrate the effectiveness of the proposed approach. A verification rate of 98.60% is obtained.

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References

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

    Article  Google Scholar 

  2. 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

    Article  Google Scholar 

  3. Blanz V, Vetter T (2003) Face recognition based on fitting a 3d morphable model. IEEE Trans Pattern Anal Mach Intell 25(9):1063–1074

    Article  Google Scholar 

  4. Cao X, Wei X, Han Y, Lin D (2015) Robust face clustering via tensor decomposition. IEEE Trans Cybern 45(11):2546–2557

    Article  Google Scholar 

  5. Chouchane A, Belahcène M, Ouamane A, Bourennane S (2016) Evaluation of histograms local features and dimensionality reduction for 3D face verification. Journal of Information Processing Systems, pp 1–21. https://hal.archives-ouvertes.fr/hal-01281110

  6. De Lathauwer L, De Moor B, Vandewalle J (2000) A multilinear singular value decomposition. SIAM J Matrix Anal Appl 21(4):1253–1278

    Article  MathSciNet  MATH  Google Scholar 

  7. Dibeklioglu H, Salah AA, Akarun L (2008) 3d facial landmarking under expression, pose, and occlusion variations. In: 2008. BTAS 2008. 2nd IEEE International Conference on Biometrics: Theory, Applications and Systems. IEEE, pp 1–6

  8. Faltemier TC, Bowyer KW, Flynn PJ (2008) Rotated profile signatures for robust 3d feature detection. In: 2008. FG’08. 8th IEEE International Conference on Automatic Face & Gesture Recognition. IEEE, pp 1–7

  9. Ji Q (2002) 3d face pose estimation and tracking from a monocular camera. Image Vis Comput 20(7):499–511. https://doi.org/10.1016/S0262-8856(02)00024-0. http://www.sciencedirect.com/science/article/pii/S0262885602000240

    Article  Google Scholar 

  10. Lai Z, Xu Y, Chen Q, Yang J, Zhang D (2014) Multilinear sparse principal component analysis. IEEE Trans Neural Netw Learn Syst 25(10):1942–1950

    Article  Google Scholar 

  11. Lai Z, Xu Y, Yang J, Tang J, Zhang D (2013) Sparse tensor discriminant analysis. Trans Img Proc 22(10):3904–3915. https://doi.org/10.1109/TIP.2013.2264678

    Article  MathSciNet  MATH  Google Scholar 

  12. Letexier D (2009) Filtrages tensoriels adaptatifs pour la restauration d’images multidimensionnelles. Ph.D. thesis, Aix-Marseille 3

  13. 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. https://doi.org/10.1109/TIP.2002.999679

    Article  Google Scholar 

  14. Liu X, Chen T (2005) Pose-robust face recognition using geometry assisted probabilistic modeling. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol 1. IEEE, pp 502–509

  15. Lu H, Plataniotis KN, Venetsanopoulos AN (2008) MPCA: Multilinear principal component analysis of tensor objects. IEEE Trans Neural Netw 19(1):18–39

    Article  Google Scholar 

  16. Marcolin F, Vezzetti E (2017) Novel descriptors for geometrical 3d face analysis. Multimed Tools Appl 76(12):13,805–13,834. https://doi.org/10.1007/s11042-016-3741-3

    Article  Google Scholar 

  17. Mian A, 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

    Article  Google Scholar 

  18. Ming Y (2014) Rigid-area orthogonal spectral regression for efficient 3d face recognition. Neurocomputing 129:445–457

    Article  Google Scholar 

  19. 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

  20. Ouamane A, Belahcene M, Benakcha A, Bourennane S, Taleb-Ahmed A (2016) Robust multimodal 2D and 3D face authentication using local feature fusion. Signal, Image Video Process 10(1):129–137

    Article  Google Scholar 

  21. Ouamane A, Chouchane A, Boutellaa E, Belahcene M, Bourennane S, Hadid A (2017) Efficient tensor-based 2d + 3d face verification. IEEE Transactions on Information Forensics and Security

  22. Passalis G, Perakis P, Theoharis T, Kakadiaris IA (2011) Using facial symmetry to handle pose variations in real-world 3d face recognition. IEEE Trans Pattern Anal Mach Intell 33(10):1938–1951

    Article  Google Scholar 

  23. Rajwade A, Levine MD (2006) Facial pose from 3d data. Image Vis Comput 24(8):849–856

    Article  Google Scholar 

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

  25. Vasilescu MAO (2009) A multilinear (tensor) algebraic framework for computer graphics, computer vision, and machine learning. Ph.D. thesis, Citeseer

  26. Vezzetti E, Marcolin F, Tornincasa S, Maroso P (2016) Application of geometry to rgb images for facial landmark localisation - a preliminary approach. Int J Biom 8(3-4):216–236. https://doi.org/10.1504/IJBM.2016.082597

    Article  Google Scholar 

  27. Wang X, Ruan Q, Ming Y (2010) 3d face recognition using corresponding point direction measure and depth local features. In: 2010 IEEE 10th International Conference on Signal Processing (ICSP). IEEE, pp 86–89

  28. 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

    Article  MATH  Google Scholar 

  29. Xu C, Wang Y, Tan T, Quan L (2005) 3D face recognition based on G-H shape variation. Springer, Berlin, pp 233–243. https://doi.org/10.1007/978-3-540-30548-4_27

    Google Scholar 

  30. Xu D, Yan S, Tao D, Zhang L, Li X, Zhang HJ (2006) Human gait recognition with matrix representation. IEEE Trans Circ Syst Video Technol 16 (7):896–903

    Article  Google Scholar 

  31. 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

    Article  MathSciNet  Google Scholar 

  32. Zhang X, Gao Y (2009) Face recognition across pose: A review. Pattern Recogn 42(11):2876–2896

    Article  Google Scholar 

  33. Zhu X, Lei Z, Liu X, Shi H, Li SZ (2016) Face alignment across large poses: A 3d solution. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 146–155. https://doi.org/10.1109/CVPR.2016.23

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Authors and Affiliations

  1. Université de Biskra, Biskra, Algeria

    Ammar Chouchane, Abdelmalik Ouamane & Mebarka Belahcene

  2. Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland

    Elhocine Boutellaa

  3. Institut Fresnel, Université de Marseille, Marseille, France

    Salah Bourennane

Authors
  1. Ammar Chouchane
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  2. Abdelmalik Ouamane
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  3. Elhocine Boutellaa
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  4. Mebarka Belahcene
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  5. Salah Bourennane
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Correspondence to Ammar Chouchane.

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Chouchane, A., Ouamane, A., Boutellaa, E. et al. 3D face verification across pose based on euler rotation and tensors. Multimed Tools Appl 77, 20697–20714 (2018). https://doi.org/10.1007/s11042-017-5478-z

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  • DOI: https://doi.org/10.1007/s11042-017-5478-z

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