http://iet.metastore.ingenta.com
1887

Class-wise two-dimensional PCA method for face recognition

Class-wise two-dimensional PCA method for face recognition

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Computer Vision — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Interests in biometric identification systems have led to many face recognition task-oriented studies. These studies often address the detection of face images taken from a camera and the recognition of faces via extracted meaningful features. To meet the requirement of defining data with fewer features, principal component analysis (PCA)-based techniques are widely used due to their efficiency and simplicity. There is a remarkable interest in the used efficiency of PCA by extending this traditional technique with various aspects. From this viewpoint, this study is specifically focused on the PCA-based face recognition techniques. By enhancing the methods in the reviewed studies, a novel class-wise two-dimensional PCA-based face recognition algorithm is presented in this study. Unlike the traditional method, this method generates more than one subspace considering within-class scattering. A system based on the presented approach can successively detect and recognise faces in not only images but also in video files. In addition, analyses were conducted to evaluate the efficiency of the proposed algorithm and its extension comparing with other addressed PCA-based methods. On the basis of the experimental results, it is clear to say that the presented approach and its extension are superior to the compared PCA-based algorithms.

References

    1. 1)
      • 1. Abate, A.F., Nappi, M., Riccio, D., et al: ‘2D and 3D face recognition: a survey’, Pattern Recognit. Lett., 2007, 28, pp. 18851906.
    2. 2)
      • 2. Patil, A.M., Kolhe, S.R., Patil, P.M.: ‘2D face recognition techniques: a survey’, Int. J. Mach. Intell., 2010, 2, (1), pp. 7483.
    3. 3)
      • 3. Sirovich, L., Kirby, M.: ‘Low-dimensional procedure for the characterization of human faces’, J. Opt. Soc. Am. A, 1987, 4, pp. 519524.
    4. 4)
      • 4. Kirby, M., Sirovich, L.: ‘Application of the Karhunen–Loeve procedure for the characterization of human faces’, IEEE Trans. Pattern Anal. Mach. Intell., 1990, 12, pp. 103108.
    5. 5)
      • 5. Turk, M., Pentland, A.: ‘Eigenfaces for recognition’, J. Cogn. Neurosci., 1991, 3, (1), pp. 7186.
    6. 6)
      • 6. Gottumukkal, R., Asari, V.K.: ‘An improved face recognition technique based on modular PCA approach’, Pattern Recognit. Lett., 2004, 25, (4), pp. 429436.
    7. 7)
      • 7. Yang, J., Zhang, D., Frangi, A.F., et al: ‘Two-dimensional PCA: a new approach to appearance-based face representation and recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2004, 26, (1), pp. 131137.
    8. 8)
      • 8. Wang, L., Wang, X., Zhang, X., et al: ‘The equivalence of two-dimensional PCA to line-based PCA’, Pattern Recognit. Lett., 2005, 26, (1), pp. 5760.
    9. 9)
      • 9. Zhang, D., Zhou, Z.-H.: ‘(2D)2PCA: 2-directional 2-dimensional PCA for efficient face representation and recognition’, Neurocomputing, 2005, 69, (1–3), pp. 224231.
    10. 10)
      • 10. Zuo, W., Zhang, D., Wang, K.: ‘Bidirectional PCA with assembled matrix distance metric for image recognition’, IEEE Trans. Syst. Man Cybern. B, Cybern., 2006, 36, pp. 863872.
    11. 11)
      • 11. Mashhoori, A., Jahromi, M.Z.: ‘Block-wise two-directional 2DPCA with ensemble learning for face recognition’, Neurocomputing, 2013, 108, pp. 111117.
    12. 12)
      • 12. Safayani, M., Manzuri Shalmani, M., Khademi, M.: ‘Extended two-dimensional PCA for efficient face representation and recognition’. 2008 Fourth Int. Conf. on Intelligent Computer Communication and Processing, 2008, pp. 295298.
    13. 13)
      • 13. Nedevschi, S., Peter, I.: ‘An improved PCA type algorithm applied in face recognition’. 2010 IEEE Int. Conf. on Proc. Intelligent Computer Communication and Processing (ICCP), 2010, pp. 259262.
    14. 14)
      • 14. Yang, Q., Ding, X.: ‘Symmetrical PCA in face recognition’. Proc. IEEE Int. Conf. on Image Processing (ICIP'02), vol. 2, 2002, pp. 97100.
    15. 15)
      • 15. Ding, M., Lu, C., Lin, Y., et al: ‘Symmetry based two-dimensional principal component analysis for face recognition’, Adv. Neural Netw. ISNN 2007, 2007, 4492, pp. 10481055.
    16. 16)
      • 16. Vasilescu, M., Terzopoulos, D.: ‘Multilinear image analysis for facial recognition’. Proc. Int. Conf. on Pattern Recognition (ICPR'02), IEEE Computer Society, vol. 2, 2002, pp. 511514.
    17. 17)
      • 17. Cai, D., He, X., Han, J.: ‘Subspace learning based on tensor analysis’. 2005.
    18. 18)
      • 18. Quanxue, G., Yiying, L., Yamin, L., et al: ‘Directional principal component analysis for image matrix’. Proc. Int. Conf. on Computational Aspects of Social Networks, September 2010, pp. 374377.
    19. 19)
      • 19. Zhang, D., Zhou, Z.-H., Chen, S.: ‘Diagonal principal component analysis for face recognition’, Pattern Recognit., 2006, 39, (1), pp. 140142.
    20. 20)
      • 20. Kumar, A.P., Das, S., Kamakoti, V.: ‘Face recognition using weighted modular principle component analysis’. Proc. 11th Int. Conf. Neural Information Processing (ICONIP 2004), 2004, pp. 362367.
    21. 21)
      • 21. Scholkopf, B., Smola, A., Muller, K.R.: ‘Nonlinear component analysis as a kernel eigenvalue problem’, Neural Comput., 1998, 10, (5), pp. 12991319.
    22. 22)
      • 22. Yang, M., Ahuja, N., Kriegman, D.: ‘Face recognition using kernel eigenfaces’. Proc. Int. Conf. on Image Processing, 2000, pp. 3740.
    23. 23)
      • 23. Nhat, V.D.M., Lee, S.: ‘Kernel-based 2DPCA for face recognition’. Proc. IEEE Int. Symp. on Signal Processing and Information Technology, December 2007, pp. 3539.
    24. 24)
      • 24. Ozawa, S., Takeuchi, Y., Abe, S.: ‘A fast incremental kernel principal component analysis for online feature extraction’. Proc. Pacific Rim Int. Conf. on Artificial Intelligence, 2010, pp. 487497.
    25. 25)
      • 25. Choi, Y., Ozawa, S., Lee, M.: ‘Incremental two-dimensional kernel principal component analysis’, Neurocomputing, 2014, 134, pp. 280288.
    26. 26)
      • 26. Yang, W., Sun, C., Zhang, L., et al: ‘Laplacian bidirectional PCA for face recognition’, Neurocomputing, 2010, 74, (1), pp. 487493.
    27. 27)
      • 27. Li, X., Pang, Y., Yuan, Y.: ‘L1-norm-based 2DPCA’, IEEE Trans. Syst. Man Cybern. B, Cybern.: Publ. IEEE Syst. Man Cybern. Soc., 2010, 40, (4), pp. 11701175.
    28. 28)
      • 28. Ke, Q., Kanade, T.: ‘Robust L1 norm factorization in the presence of outliers and missing data by alternative convex programming’. Proc. 2005 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR'05), vol. 1, 2005, pp. 739746.
    29. 29)
      • 29. Kwak, N.: ‘Principal component analysis based on L1-norm maximization’, IEEE Trans. Pattern Anal. Mach. Intell., 2008, 30, (9), pp. 16721680.
    30. 30)
      • 30. Wang, H., Wang, J.: ‘2DPCA with L1-norm for simultaneously robust and sparse modelling’, Neural Netw., 2013, 46, pp. 190198.
    31. 31)
      • 31. Turhal, Ü.Ç., Duysak, A.: ‘Cross grouping strategy based 2DPCA method for face recognition’, Appl. Soft Comput., 2015, 29, pp. 270279.
    32. 32)
      • 32. Cui, K., Gao, Q., Zhang, H., et al: ‘Merging model-based two-dimensional principal component analysis’, Neurocomputing, 2015, 168, pp. 11981206.
    33. 33)
      • 33. Haifeng, S., Guangsheng, C., Hairong, W., et al: ‘The improved (2D)2 PCA algorithm and its parallel implementation based on image block’, Microprocess. Microsyst., 2016, 47, pp. 170177.
    34. 34)
      • 34. Viola, P., Jones, M.J.: ‘Robust real-time face detection’, Int. J. Comput. Vis., 2004, 57, (2), pp. 137154.
    35. 35)
      • 35. Pizer, S.M., Amburn, E.P., Austin, J.D., et al: ‘Adaptive histogram equalization and its variations’, Comput. Vis. Graph. Image Process., 1987, 39, pp. 355368.
    36. 36)
      • 36. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: ‘Eigenfaces vs. fisherfaces: recognition using class specific linear projection’, IEEE Trans. Pattern Anal. Mach. Intell., 1997, 19, (7), pp. 711720.
    37. 37)
      • 37. Samaria, F.S., Harter, A.C.: ‘Parameterisation of a stochastic model for human face identification’. Proc. of the Second IEEE Workshop on Proc. Applications of Computer Vision, 1994, 1994, pp. 138142.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2016.0135
Loading

Related content

content/journals/10.1049/iet-cvi.2016.0135
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address