Skip to main content

Advertisement

Log in

Human face recognition by adaptive processing of tree structures representation

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This paper describes a novel method of facial representation and recognition based upon adaptive processing of tree structures. Instead of the conventional flat vector representation for a face, a neural network approach-based technique is proposed to transform the Localised Gabor Feature (LGF) vectors extracted from human facial components into Human Face Tree Structure (HFTS) to represent a human face. A structural training algorithm is assigned to train and recognize the face identity in this HFTS representation with the corresponding LGF vectors. By benchmarking using the tested public face databases presented in this paper, our approach is able to achieve accuracy up to 90% under different scenarios of lighting conditions and posture orientations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv 35(4):399–458

    Article  Google Scholar 

  2. Yang M-H, Kriegman DJ, Ahuja N (2002) Detecting faces in images: a survey. IEEE Trans Pattern Anal Mach Intell 24(1):34–58

    Article  Google Scholar 

  3. Woodward JD Jr, Orlans NM, Higgins PT (2003) Identity assurance in the information age biometrics. McGraw-Hill, Osborne

    Google Scholar 

  4. Ekman P (1992) Facial expression of emotion: an old controversy and new findings. Philos Trans R Soc Lond 335:63–69

    Article  Google Scholar 

  5. Kanade T (1973) Picture processing by computer complex and recognition of human faces. Kyoto University, Japan

    Google Scholar 

  6. Gökberk B, Irfanoglu MO, Akarun L, Alpaydin E (2003) Optimal global kernel location selection for face recognition. In: Proceedings of IEEE international conference on image processing 1:677–680

  7. Brunelli R, Poggio T (1993) Face recognition: features versus templates. IEEE Trans Pattern Anal Mach Intell 15:1042–1052

    Article  Google Scholar 

  8. Gao Y, Leung MKH (2002) Face recognition using line edge map. IEEE Trans Pattern Anal Mach Intell 24(6):764–779

    Article  Google Scholar 

  9. Singh SK, Chauhan DS, Vatsa M, Singh R (2003) A robust skin color based face detection algorithm. Tamkang J Sci Eng 6(4):227–234

    Google Scholar 

  10. Wiskott L, Fellous J-M, Kruger N, von der Malsburg C (1997) Face recognition by elastic bunch graph matching. IEEE Trans Pattern Anal Mach Intell 19(7):775–779

    Article  Google Scholar 

  11. Viola P, Jones M (2001) Robust real-time object detection, In: Proceedings of 2nd international workshop on statistical and computational theories of vision—modeling, learning, computing, and sampling, Vancouver, Canada

  12. Rowley HA, Baluja S, Kanade T (1998) Neural network based face detection. IEEE Trans Pattern Anal Mach Intell 20:20–38

    Article  Google Scholar 

  13. Liu C (2003) A Bayesian discriminating features method for face detection. IEEE Trans Pattern Anal Mach Intell 25(6):725–740

    Article  Google Scholar 

  14. Phillips PJ, Moon H, Rauss P, Rizvi SA (1997) The FERET evaluation methodology for face recognition algorithms. In: Proceedings of IEEE conference on computer vision and pattern recognition, pp 137–143

  15. Garcia C, Delakis M (2004) Convolutional face finder: a neural architecture for fast and robust face detection. IEEE Trans Pattern Anal Mach Intell 26(11):1408–1423

    Article  Google Scholar 

  16. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3:71–86

    Article  Google Scholar 

  17. Lawrence S, Giles CL, Tsoi AC, Back AD (1997) Face recognition: a convolution neural network approach. IEEE Trans Neural Netw 8:98–113

    Article  Google Scholar 

  18. Lin SH, Kung SY, Lin LJ (1997) Face recognition/detection by probabilistic decision-based neural network. IEEE Trans Neural Netw 8(1):114–132

    Article  Google Scholar 

  19. Er MJ, Wu S, Lu J (2002) Face recognition with radial basis function (RBF) neural networks. IEEE Trans Neural Netw 13(3):697–710

    Article  Google Scholar 

  20. Pankanti S, Bolle RM, Jain A (2000) Guest editors’ introduction: biometrics—the future of identification. Computer 33(2):46–49

    Article  Google Scholar 

  21. Adini Y, Moses Y, Ullman S (1997) Face recognition: the problem of compensating for changes in illumination direction. IEEE Trans Pattern Anal Mach Intell 19:721–732

    Article  Google Scholar 

  22. Beymer DJ (1993) Face recognition under varying pose. Technical report 1461, MIT AI Laboratory

  23. Vetter T, Poggio T (1997) Linear object classes and image synthesis from a single example image. IEEE Trans Pattern Anal Mach Intell 19:733–742

    Article  Google Scholar 

  24. Jain A (1989) Fundamentals of digital image processing. Prentice Hall, Englewood Cliffs

    MATH  Google Scholar 

  25. Liu C, Wechsler H (2003) Independent component analysis of gabor features for face recognition. IEEE Trans Neural Netw 14(4):919–928

    Article  Google Scholar 

  26. 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):467–472

    Article  Google Scholar 

  27. Jing X-Y, Zhang D, Yao Y-F (2003) Improvements on the linear discrimination technique with application to face recognition. Pattern Recognit Lett 24:2695–2701

    Article  Google Scholar 

  28. Tsoi AC (1998) Adaptive processing of data structure: an expository overview and comments, faculty informatics. University of Wollongong, Wollongong, Australia

  29. Sperduti A, Starita A (1997) Supervised neural networks for classification of structures. IEEE Trans Neural Netw 8:714–735

    Article  Google Scholar 

  30. Frasconi P, Gori M, Sperduti A (1998) A general framework for adaptive processing of data structures. IEEE Trans Neural Netw 9:768–785

    Article  Google Scholar 

  31. Cho S-Y, Chi Z, Siu W-C, Tsoi AC (2003) An improved algorithm for learning long-term dependency problems in adaptive processing of data structures. IEEE Trans Neural Netw 14(4):781–793

    Article  Google Scholar 

  32. Cho S-Y, Wong J-J (2005) Robust facial recognition by localised gabor features, In: Proceedings of international workshop for advanced image technology, Cheju National University, Jeju Island, Korea

  33. Georghiades AS, Belhumeur PN, Kriegman DJ (2001) From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans Pattern Anal Mach Intell 23(6):643–660

    Article  Google Scholar 

  34. Platt J (1998) Fast training of support vector machines using sequential minimal optimization. In: Scholkopf B, Burges C, Smola A (eds) Advances in kernel methods—suppoort vector learning. MIT Press, Cambridge, pp 185–208

  35. Aha D, Kibler D (1991) Instance based learning algorithms. Mach Learn 6:37–66

    Google Scholar 

  36. John GH, Langley P (1995) Estimating continuous distributions in bayesian classifiers. In: Proceedings of the 11th conference on uncertainty in artificial intelligence. Morgan Kaufmann, San Mateo, pp 338–345

  37. Samaria F, Harter AC (1994) Parameterisation of a stochastic model for human face identification. In: Proceedings of 2nd IEEE workshop applications of computer vision, pp 138–142

  38. Daugman JG (1985) Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional cortical filters. J Opt Soc Am 2(7):1160–1167

    Google Scholar 

  39. Daugman JG (1988) Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression. IEEE Trans Pattern Anal Mach Intell 36:1169–1179

    MATH  Google Scholar 

  40. Jones J, Palmer L (1987) An evaluation of the two dimensional Gabor Filter model of simple receptive fields in cat striate cortex. J Neurophysiol 58(6):1233–1258

    Google Scholar 

  41. Marcelja S (1980) Mathematical description of the responses of simple cortical cells. J Opt Soc Am 70:1297–1300

    Article  MathSciNet  Google Scholar 

  42. Manjunath BS, Ma WY (1996) Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18(8):837–842

    Article  Google Scholar 

  43. Phillips PJ, Hyeonjoon Moon, Syed A. Rizvi, Patrick J. Rauss (2000) The FERET evaluation methodology for face-recognition algorithms. IEEE Trans PAMI 22(10):1090–1104

    Google Scholar 

  44. Bengio Y, Simard P, Frasconi P (1994) Learning long term dependencies with gradient descent is difficult. IEEE Trans Neural Netw 5(2):157–166

    Article  Google Scholar 

  45. Witten IH, Frank E (2000) Data mining: practical machine learning tools with Java implementations. Morgan Kaufmann, San Francisco

    Google Scholar 

  46. Guo G, Li SZ, Chan K (2000) Face recognition by support vector machines. In: Proceedings of IEEE conference on automatic face and gesture recognition, pp 196–201

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Siu-Yeung Cho.

Additional information

This work is supported by the NTU SCE start-up grant (CE-SUG 10/03).

Rights and permissions

Reprints and permissions

About this article

Cite this article

Cho, SY., Wong, JJ. Human face recognition by adaptive processing of tree structures representation. Neural Comput & Applic 17, 201–215 (2008). https://doi.org/10.1007/s00521-007-0108-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-007-0108-8

Keywords

Navigation