© The Institution of Engineering and Technology
This study proposes an algorithm is proposed to quantitatively evaluate the performance of three-dimensional (3D) holistic face recognition algorithms when various denoising methods are used. First, a method is proposed to model the noise on the 3D face datasets. The model not only identifies those regions on the face which are sensitive to the noise but can also be used to simulate noise for any given 3D face. Then, by incorporating the noise model in a novel 3D face recognition pipeline, seven different classification and matching methods and six denoising techniques are used to quantify the face recognition algorithms performance for different powers of the noise. The outcome: (i) shows the most reliable parameters for the denoising methods to be used in a 3D face recognition pipeline; (ii) shows which parts of the face are more vulnerable to noise and require further post-processing after data acquisition; and (iii) compares the performance of three different categories of recognition algorithms: training-free matching-based, subspace projection-based and training-based (without projection) classifiers. The results show the high performance of the bootstrap aggregating tree classifiers and median filtering for very high intensity noise. Moreover, when different noisy/denoised samples are used as probes or in the gallery, the matching algorithms significantly outperform the training-based (including the subspace projection) methods.
References
-
-
1)
-
29. Dietterich, T.: ‘Ensemble methods in machine learning’. Multiple Classifier Systems, Berlin Heidelberg, 2000 (, 1857), pp. 1–15.
-
2)
-
28. Donoho, D.L., Johnstone, I.M.: ‘Ideal spatial adaptation via wavelet shrinkage’, Biometrika, 1994, 81, pp. 425–455 (doi: 10.1093/biomet/81.3.425).
-
3)
-
30. Ng, A.Y., Jordan, M.I.: ‘On discriminative vs. generative classifiers: a comparison of logistic regression and naive Bayes’. Advances in Neural Information Processing Systems 14, 2002, pp. 841–848.
-
4)
-
14. Xu, C., Li, S., Tan, T., Quan, L.: ‘Automatic 3D face recognition from depth and intensity Gabor features’, Pattern Recognit., 2009, 42, (9), pp. 1895–1905 (doi: 10.1016/j.patcog.2009.01.001).
-
5)
-
8. Lei, Y., Bennamoun, M., Hayat, M., Guo, Y.: ‘An efficient 3D face recognition approach using local geometrical signatures’, Pattern Recognit., 2013, 47, (2), pp. 509–524 (doi: 10.1016/j.patcog.2013.07.018).
-
6)
-
21. Llonch, R., Kokiopoulou, E., Tosic, I., Frossard, P.: ‘3D face recognition using sparse spherical representations’. 19th IEEE Int. Conf. on Pattern Recognition, 2008, pp. 1–4.
-
7)
-
40. Berretti, S., Del Bimbo, A., Pala, P.: ‘Sparse matching of salient facial curves for recognition of 3-D faces with missing parts’, IEEE Trans. Inf. Forensics Sec., 2013, 8, (2), pp. 374–389 (doi: 10.1109/TIFS.2012.2235833).
-
8)
-
4. Perona, P., Malik, J.: ‘Scale-space and edge detection using anisotropic diffusion’, IEEE Trans. Pattern Anal. Mach. Intell., 1990, 12, (7), pp. 629–639 (doi: 10.1109/34.56205).
-
9)
-
17. Emambakhsh, M., Evans, A.: ‘Self-dependent 3D face rotational alignment using the nose region’. Proc. of the Fourth IET Int. Conf. on Imaging for Crime Detection and Prevention (ICDP), 2011, pp. 1–6.
-
10)
-
13. Wang, Y., Liu, J., Tang, X.: ‘Robust 3D face recognition by local shape difference boosting’, IEEE Trans. Pattern Anal. Mach. Intell., 2010, 32, (10), pp. 1858–1870 (doi: 10.1109/TPAMI.2009.200).
-
11)
-
2. Flint, A., Dick, A., van den Hengel, A.: ‘Local 3D structure recognition in range images’, IET Comput. Vis., 2008, 2, (4), pp. 208–217 (doi: 10.1049/iet-cvi:20080037).
-
12)
-
41. Li, H., Huang, D., Morvan, J.-M., Chen, L., Wang, Y.: ‘Expression-robust 3D face recognition via weighted sparse representation of multi-scale and multi-component local normal patterns’, Neurocomputing, 2014, 133, (0), pp. 179–193 (doi: 10.1016/j.neucom.2013.11.018).
-
13)
-
15. Emambakhsh, M., Evans, A., Smith, M.: ‘Using nasal curves matching for expression robust 3D nose recognition’. Proc. of the Sixth IEEE Int. Conf. on Biometrics: Theory, Applications and Systems (BTAS), 2013, pp. 1–6.
-
14)
-
28. Belhumeur, P., Hespanha, J., Kriegman, D.: ‘Eigenfaces vs. Fisherfaces: recognition using class specific linear projection’, IEEE Trans. Pattern Anal. Mach. Intell., 1997, 19, pp. 711–720 (doi: 10.1109/34.598228).
-
15)
-
1. Colbry, D., Stockman, G.: ‘Real-time identification using a canonical face depth map’, IET Comput. Vis., 2009, 3, (2), pp. 74–92 (doi: 10.1049/iet-cvi.2008.0055).
-
16)
-
23. Lim, J.S.: ‘Two-dimensional signal and image processing’ (Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1990), p. 548.
-
17)
-
5. Kakadiaris, I., Passalis, G., Toderici, G., et al: ‘Three-dimensional face recognition in the presence of facial expressions: an annotated deformable model approach’, IEEE Trans. Pattern Anal. Mach. Intell., 2007, 29, (4), pp. 640–649 (doi: 10.1109/TPAMI.2007.1017).
-
18)
-
37. Deng, W., Hu, J., Guo, J.: ‘Extended SRC: undersampled face recognition via intraclass variant dictionary’, IEEE Trans. Pattern Anal. Mach. Intell., 2012, 34, (9), pp. 1864–1870 (doi: 10.1109/TPAMI.2012.30).
-
19)
-
39. Ng, A.Y.: ‘Feature selection, L1 vs. L2 regularization, and rotational invariance’. Proc. of the 21st Int. Conf. on Machine Learning (ICML), 2004, pp. 78–86.
-
20)
-
3. Emambakhsh, M., Gao, J., Evans, A.: ‘An evaluation of denoising algorithms for 3D face recognition’. Proc. of the Fifth IET Int. Conf. on Imaging for Crime Detection and Prevention (ICDP 2013), 2013, pp. 1–6.
-
21)
-
19. Lei, Y., , M., El-Sallam, A.A.: ‘An efficient 3D face recognition approach based on the fusion of novel local low-level features’, Pattern Recognit., 2013, 46, (1), pp. 24–37 (doi: 10.1016/j.patcog.2012.06.023).
-
22)
-
9. Mian, A., Bennamoun, M., Owens, R.: ‘An efficient multimodal 2D–3D hybrid approach to automatic face recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2007, 29, (11), pp. 1927–1943 (doi: 10.1109/TPAMI.2007.1105).
-
23)
-
16. Mohammadzade, H., Hatzinakos, D.: ‘Iterative closest normal point for 3D face recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35, (2), pp. 381–397 (doi: 10.1109/TPAMI.2012.107).
-
24)
-
20. Drira, H., Amor, B.B., Srivastava, A., Daoudi, M., Slama, R.: ‘3D face recognition under expressions, occlusions, and pose variations’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35, (9), pp. 2270–2283 (doi: 10.1109/TPAMI.2013.48).
-
25)
-
7. Turk, M., Pentland, A.: ‘Eigenfaces for recognition’, J. Cogn. Neurosci, 1991, 13, (1), pp. 71–86 (doi: 10.1162/jocn.1991.3.1.71).
-
26)
-
34. Struc, V.: ‘The PhD face recognition toolbox’, February2012. .
-
27)
-
18. Moorhouse, A., Evans, A., Atkinson, G., Sun, J., Smith, M.: ‘The nose on your face may not be so plain: using the nose as a biometric’. Third IET Int. Conf. on Crime Detection and Prevention (ICDP), 2009, pp. 1–6.
-
28)
-
6. Passalis, G., Perakis, P., Theoharis, T., Kakadiaris, I.: ‘Using facial symmetry to handle pose variations in real-world 3D face recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2011, 33, (10), pp. 1938–1951 (doi: 10.1109/TPAMI.2011.49).
-
29)
-
38. Ke, Q., Kanade, T.: ‘Robust L1-norm factorization in the presence of outliers and missing data’. Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2005, p. 592599.
-
30)
-
33. Savran, A., Alyüz, N., Dibekliğlu, H., et al: ‘Bosphorus database for 3D face analysis’. Biometrics and Identity Management, Berlin/Heidelberg, 2008, vol. 5372, pp. 47–56.
-
31)
-
4. Efraty, B., Bilgazyev, E., Shah, S., Kakadiaris, I.A.: ‘Profile-based 3D-aided face recognition’, Pattern Recognit., 2012, 45, (1), pp. 43–53 (doi: 10.1016/j.patcog.2011.07.010).
-
32)
-
11. Li, X., Da, F.: ‘Efficient 3D face recognition handling facial expression and hair occlusion’, Image Vis. Comput., 2012, 30, (9), pp. 668–679 (doi: 10.1016/j.imavis.2012.07.011).
-
33)
-
36. Wright, J., Yang, A., Ganesh, A., Sastry, S., Ma, Y.: ‘Robust face recognition via sparse representation’, IEEE Trans. Pattern Anal. Mach. Intell., 2009, 31, (2), pp. 210–227 (doi: 10.1109/TPAMI.2008.79).
-
34)
-
10. Chang, K.I., Bowyer, K., Flynn, P.: ‘Adaptive rigid multi-region selection for handling expression variation in 3D face recognition’. IEEE Conf. on Computer Vision and Pattern Recognition Workshops (CVPRW), 2005, p. 157.
-
35)
-
12. Ming, Y., Ruan, Q.: ‘Robust sparse bounding sphere for 3D face recognition’, Image Vis. Comput., 2012, 30, (8), pp. 524–534 (doi: 10.1016/j.imavis.2012.05.001).
-
36)
-
32. Colombo, A., Cusano, C., Schettini, R.: ‘UMB-DB: a database of partially occluded 3D faces’. IEEE Int. Conf. on Computer Vision (ICCV), 2011, pp. 2113–2119.
-
37)
-
7. Perakis, P., Passalis, G., Theoharis, T., Kakadiaris, I.: ‘3D facial landmark detection under large yaw and expression variations’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35, (7), pp. 1552–1564 (doi: 10.1109/TPAMI.2012.247).
-
38)
-
35. D'Almeida, F.: ‘Nonlinear diffusion toolbox’, July2003. .
-
39)
-
42. Zafeiriou, S., Hansen, M., Atkinson, G., et al: ‘The Photoface database’. Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition Workshops (CVPRW), 2011, pp. 132–139.
-
40)
-
25. Emambakhsh, M., Ebrahimnezhad, H., Sedaaghi, M.: ‘Integrated region-based segmentation using color components and texture features with prior shape knowledge’, Int. J. Appl. Math. Comput. Sci., 2010, 20, (4), pp. 711–726 (doi: 10.2478/v10006-010-0054-y).
-
41)
-
31. Blei, D.M., Ng, A.Y., Jordan, M.I.: ‘Latent Dirichlet allocation’, J. Mach. Learn. Res., 2003, 3, pp. 993–1022.
-
42)
-
26. Sun, X., Rosin, P., Martin, R., Langbein, F.: ‘Noise in 3D laser range scanner data’. IEEE Int. Conf. on Shape Modeling and Applications, 2008, pp. 37–45.
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