Accesso libero

3D Face Factorisation for Face Recognition Using Pattern Recognition Algorithms

INFORMAZIONI SU QUESTO ARTICOLO

Cita

1. Dantcheva, A., P. Elia, A. Ross. What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics. – IEEE Transactions on Information Forensics and Security, Vol. 11, 2016, No 3, pp. 441-467.10.1109/TIFS.2015.2480381Search in Google Scholar

2. Nassih, B., M. Ngadi, A. Amine, A. El-Attar. New Proposed Fusion between DCT for Feature Extraction and NSVC for Face Classification. – Cybernetics and Information Technologies, Vol. 18, 2018, No 2, pp. 89-97.10.2478/cait-2018-0030Search in Google Scholar

3. Thompson, P. Margaret Thatcher: A New Illusion. Perception, 1980.10.1068/p0904836999452Search in Google Scholar

4. Klingenberg, C. P. Morphometric Integration and Modularity in Configurations of Land-Marks: Tools for Evaluating a Priori Hypotheses. – Evolution & Development, Vol. 11, 2009, No 4, pp. 405-421.10.1111/j.1525-142X.2009.00347.x277693019601974Search in Google Scholar

5. Xu, C., Y. Wang, T. Tan, et al. Automatic 3d Face Recognition Combining Global Geometric Features with Local Shape Variation Information. – In: Proc. of 6th IEEE International Conference on Automatic Face and Gesture Recognition, 2004. IEEE, 2004, pp. 308-313.Search in Google Scholar

6. Benedikt, L., D. Cosker, P. L. Rosin, et al. Assessing the Uniqueness and Permanence of Facial Actions for Use in Biometric Applications. – IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, Vol. 40, 2010, No 3, pp. 44-460.10.1109/TSMCA.2010.2041656Search in Google Scholar

7. Blanz, V., T. Vetter. A Morphable Model for the Synthesis of 3d Faces. – In: Proc. of 26th Annual Conference on Computer Graphics and Interactive Techniques, ACM Press/Addison-Wesley Publishing Co., 1999, pp. 187-194.10.1145/311535.311556Search in Google Scholar

8. Heseltine, T., N. Pears, J. Austin. Three-Dimensional Face Recognition: An Eigen Surface Approach. – In: 2004 International Conference on Image Processing ICIP’04, 2004, Vol. 2, IEEE, 2004, pp. 1421-1424.10.5244/C.18.55Search in Google Scholar

9. Al-Osaimi, F., M. Bennamoun, A. Mian. An Expression Deformation Approach to Non-Rigid 3d Face Recognition. – International Journal of Computer Vision, Vol. 81, 2009, No 3, pp. 302-316.10.1007/s11263-008-0174-0Search in Google Scholar

10. Kakadiaris, I.A., G. Passalis, G. Toderici, et al. Three-Dimensional Face Recognition in the Presence of Facial Expressions: An Annotated Deformable Model Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, 2007, No 4, pp. 640-649.10.1109/TPAMI.2007.101717299221Search in Google Scholar

11. Zhou, S., S. Xiao. 3d Face Recognition: A Survey. – Human-Centric Computing and Information Sciences, Vol. 8, 2018, No 1, 35.10.1186/s13673-018-0157-2Search in Google Scholar

12. Amor, B. B., M. Ardabilian, L. Chen. Enhancing 3d Face Recognition by Mimic’s Segmentation. – In: Proc. of 6th International Conference on Intelligent Systems Design and Applications, Vol. 3, IEEE, 2006, pp. 150-155.10.1109/ISDA.2006.24Search in Google Scholar

13. Spreeuwers, L. Fast and Accurate 3d Face Recognition. – International Journal of Computer Vision, Vol. 93, 2011, No 3, pp. 389-414.10.1007/s11263-011-0426-2Search in Google Scholar

14. Chang, K. I., K. W. Bowyer, P. J. Flynn. Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression. – IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, 2006, No 10, pp. 1695-1700.10.1109/TPAMI.2006.210Search in Google Scholar

15. Zhong, C., Z. Sun, T. Tan. Robust 3d Face Recognition Using Learned Visual Codebook. – In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2007, pp. 1-6.10.1109/CVPR.2007.383279Search in Google Scholar

16. Xie, Y. L., P. K. Hopke, P. Paatero. Positive Matrix Factorisation Applied to a Curve Resolution Problem. – Journal of Chemometrics: A Journal of the Chemometrics Society, Vol. 12, 1998, No 6, pp. 357-364.10.1002/(SICI)1099-128X(199811/12)12:6<357::AID-CEM523>3.0.CO;2-SSearch in Google Scholar

17. Li, X., B. Shen, B. D. Liu, et al. Ranking-Preserving Low-Rank Factorisation for Image Annotation with Missing Labels. – IEEE Transactions on Multimedia, Vol. 20, 2018, No 5, pp. 1169-1178.10.1109/TMM.2017.2761985Search in Google Scholar

18. Wang, Y., X. Lin, L. Wu, et al. Robust Subspace Clustering for Multi-View Data by Exploiting Correlation Consensus. – IEEE Transactions on Image Processing, Vol. 24, 2015, No 11, pp. 3939-3949.10.1109/TIP.2015.2457339Search in Google Scholar

19. Samko, O., P. L. Rosin, A. D. Marshall. Robust Automatic Data Decomposition Using a Modified Sparse NMF. – In: International Conference on Computer Vision/Computer Graphics Collaboration Techniques and Applications, Springer, 2007, pp. 225-234.10.1007/978-3-540-71457-6_21Search in Google Scholar

20. Phillips, P. J., P. J. Flynn, T. Scruggs, et al. Overview of the Face Recognition Grand Challenge. – In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition CVPR’05, 2005, Vol. 1, IEEE, 2005, pp. 947-954.Search in Google Scholar

21. Szeptycki, P., M. Ardabilian, L. Chen. A Coarse-to-_ne Curvature Analysis-Based Rotation Invariant 3d Face Landmarking. – In: 3rd International IEEE Conference on Biometrics: Theory, Applications, and Systems, 2009, IEEE, pp. 1-6.10.1109/BTAS.2009.5339052Search in Google Scholar

22. Hutton, T. J., B. F. Buxton, P. Hammond, et al. Estimating Average Growth Trajectories in Shape-Space Using Kernel Smoothing. – IEEE Transactions on Medical Imaging, Vol. 22, 2003, No 6, pp. 747-753.10.1109/TMI.2003.814784Search in Google Scholar

23. Gower, J. C. Generalized Procrustes Analysis. – Psychometrika, Vol. 40, 1975, No 1, pp. 33-51.10.1007/BF02291478Search in Google Scholar

24. Bookstein, F. L. Shape and the Information in Medical Images: A Decade of the Morphometric Synthesis. – Computer Vision and Image Understanding, Vol. 66, 1997, No 2, pp. 97-118.10.1006/cviu.1997.0607Search in Google Scholar

25. Dudani, S. A. The Distance-Weighted k-Nearest-Neighbor Rule. – IEEE Transactions on Systems, Man, and Cybernetics, 1976, No 4, pp. 325-327.10.1109/TSMC.1976.5408784Search in Google Scholar

26. Ding, C., X. He, H. D. Simon. On the Equivalence of Nonnegative Matrix Factorisation and Spectral Clustering. – In: Proc. of 2005 SIAM International Conference on Data Mining, SIAM, 2005, pp. 606-610.10.1137/1.9781611972757.70Search in Google Scholar

27. Lee, D. D., H. S. Seung. Learning the Parts of Objects by Non-Negative Matrix Factorisation. – Nature, Vol. 401, 1999, No 6755, p. 788.10.1038/4456510548103Search in Google Scholar

28. Shen, B., L. Si. Non-Negative Matrix Factorisation Clustering on Multiple Manifolds. – In: AAAI, 2010, pp. 575-580.10.1609/aaai.v24i1.7664Search in Google Scholar

29. Turk, M. A., A. P. Pentland. Face Recognition Using Eigenfaces. – In: Proc. of 1991, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 1991, pp. 586-591.Search in Google Scholar

30. Kotsiantis, S. B., I. Zaharakis, P. Pintelas. Supervised Machine Learning: A Review of Classification Techniques. – Emerging Artificial Intelligence Applications in Computer Engineering, Vol. 160, 2007, pp. 3-24.10.1007/s10462-007-9052-3Search in Google Scholar

31. Arlot, S., A. Celisse, et al. A Survey of Cross-Validation Procedures for Model Selection. – Statistics Surveys, Vol. 4, 2010, pp. 40-79.10.1214/09-SS054Search in Google Scholar

32. Lei, Y., M. Bennamoun, A. A. El-Sallam. An Efficient 3D Face Recognition Approach Based on the Fusion of Novel Local Low-Level Features. – Pattern Recognition, Vol. 46, 2013, No 1, pp. 24-37.10.1016/j.patcog.2012.06.023Search in Google Scholar

33. Faltemier, T. C., K. W. Bowyer, P. J. Flynn. A Region Ensemble for 3D Face Recognition. – IEEE Transactions on Information Forensics and Security, Vol. 3, 2008, No 1, pp. 62-73.10.1109/TIFS.2007.916287Search in Google Scholar

34. Cook, J. A., V. Chandran, C. B. Fookes. 3D Face Recognition Using Log-Gabor Templates. 2006.10.5244/C.20.79Search in Google Scholar

eISSN:
1314-4081
Lingua:
Inglese
Frequenza di pubblicazione:
4 volte all'anno
Argomenti della rivista:
Computer Sciences, Information Technology