Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

3D model-based pose invariant face recognition from multiple views

3D model-based pose invariant face recognition from multiple views

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

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(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.

A 3D model-based pose invariant face recognition method that can recognise a human face from its multiple views is proposed. First, pose estimation and 3D face model adaptation are achieved by means of a three-layer linear iterative process. Frontal view face images are synthesised using the estimated 3D models and poses. Then the discriminant ‘waveletfaces’ are extracted from these synthesised frontal view images. Finally, corresponding nearest feature space classifier is implemented. Experimental results show that the proposed method can recognise faces under variable poses with good accuracy.

References

    1. 1)
      • Yao, J., Cham, W.K.: `Efficient model-based linear head motion recovery from movies', Proc. CVPR2004, July 2004, 2, p. 414–421.
    2. 2)
      • A. Pentland . Looking at people: sensing for ubiquitous and wearable computing. IEEE Trans. Pattern Anal. Mach. Intell. , 1 , 107 - 119
    3. 3)
      • Pentland, A., Moghaddam, B., Starner, T.: `View-based modular eigenspaces for face recognition', Proc. CVPR 1994, June 1994, p. 84–91.
    4. 4)
      • Chen, Q., Wu, H.Y., Shioyama, S., Shimada, T.: `Head pose estimation using both color and feature information', Proc. 15th ICPR, September 2000, 2, p. 842–845.
    5. 5)
    6. 6)
    7. 7)
    8. 8)
      • Y. Gao , M.K.H. Leung , W. Wang , S.C. Hui . Fast face identification under varying pose from a single 2-D model view. IEE Proc., Vis. Image Signal Process. , 4 , 248 - 253
    9. 9)
    10. 10)
      • F.I. Parke . Parameterized models for facial animation. IEEE Comput. Graph. , 9 , 61 - 68
    11. 11)
    12. 12)
      • R. Ishiyama , M. Hamanaka , S. Sakamoto . An appearance model constructed on 3-D surface for robust face recognition against pose and illumination variations. IEEE Trans. Syst. Man Cybern. C, Appl. Rev. , 3 , 326 - 334
    13. 13)
      • Demir, E., Akarun, L., Alpaydin, E.: `Two-stage approach for pose invariant face recognition', Proc. ICASSP2000, June 2000, 6, p. 5–9.
    14. 14)
      • Fromherz, T.: `Face recognition: a summary of 1995–1997', Int. Computer Science Inst. ICSI TR-98-027, 1998, Berkeley.
    15. 15)
      • Blanz, V., Romdhani, S., Vetter, T.: `Face identification across different poses and illuminations with a 3D morphable model', Proc. IEEE 5th Int. Conf. Automatic Face and Gesture Recognition, 20–21 May 2002, p. 100–105.
    16. 16)
      • K.M. Lam , H. Yan . An analytic-to-holistic approach for face recognition based on a single frontal view. IEEE Trans. Pattern Anal. Mach. Intell. , 7 , 673 - 686
    17. 17)
      • V. Blanz , T. Vetter . Face recognition based on fitting a 3D morphable model. IEEE Trans. Pattern Anal. Mach. Intell. , 19 , 1063 - 1074
    18. 18)
      • C.W. Chen , J.S. Huang . Human face profile recognition from a single front view. Int. J. Pattern Recognit. Artif. Intell. , 4 , 571 - 593
    19. 19)
      • C.P. Lu , G.D. Hager , E. Mjolsness . Fast and globally convergent pose estimation from video images. IEEE Trans. Pattern Anal. Mach. Intell. , 6 , 610 - 622
    20. 20)
    21. 21)
    22. 22)
    23. 23)
    24. 24)
      • O. De Vel , S. Aeberhard . Line-based face recognition under varying pose. IEEE Trans. Pattern Anal. and Mach. Intell. , 10 , 1081 - 1088
    25. 25)
    26. 26)
      • A. Ansar , K. Daniilidis . Linear pose estimation from points or lines. IEEE Trans. Pattern Anal. Mach. Intell. , 5 , 578 - 589
    27. 27)
      • R. Schaback , M. Daelhen , T. Lyche , L.L. Shumaker . (1995) Creating surfaces from scattered data using radial basis functions, Mathematical methods in computer-aided geometric design III.
    28. 28)
    29. 29)
      • G. Wolberg . (1990) Digital image warping.
    30. 30)
      • Huang, F.J., Zhou, Z.H., Zhang, H.J., Chen, T.: `Pose invariant face recognition', Proc. 4th IEEE Int. Conf. Automatic Face and Gesture Recognition, March 2000, p. 245–250.
    31. 31)
      • D.G. Lowe . Fitting parameterized three-dimensional models to images. IEEE Trans. Pattern Anal. Mach. Intell. , 5 , 441 - 450
    32. 32)
    33. 33)
      • P.N. Belhumeur , J.P. Hespanha , D.J. Kriegman . Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. , 7 , 711 - 720
    34. 34)
      • Murase, H., Nayar, S.K.: `Learning and recognition of 3D objects from appearance', IEEE 2nd Qualitative Vision Workshop, June 1993, New York, NY.
    35. 35)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi_20060014
Loading

Related content

content/journals/10.1049/iet-cvi_20060014
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address