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3D ear shape reconstruction and recognition for biometric applications

Abstract

This paper presents a new method based on a generalized neural reflectance (GNR) model for enhancing ear recognition under variations in illumination. It is based on training a number of synthesis images of each ear taken at single lighting direction with a single view. The way of synthesizing images can be used to build training cases for each ear under different known illumination conditions from which ear recognition can be significantly improved. Our training algorithm assigns to recognize the ear by similarity measure on ear features extracting firstly by the principal component analysis method and then further processing by the Fisher’s discriminant analysis to acquire lower-dimensional patterns. Experimental results conducted on our collected ear database show that lower error rates of individual and symmetry are achieved under different variations in lighting. The recognition performance of using our proposed GRN model significantly outperforms the performance that without using the proposed GNR model.

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References

  1. 1.

    Iannarelli, A.: Ear Identification. Paramount Publishing Company, Fremont (1989)

    Google Scholar 

  2. 2.

    Yan, P., Bowyer, K.W.: Biometric recognition using 3D ear shape. IEEE Trans. PAMI 29(8), 1297–1307 (2007)

    Article  Google Scholar 

  3. 3.

    Burge, M., Burger W.: Ear biometrics in computer vision. In: 15th International Conference of Pattern Recognition, ICPR 2000, pp. 826–830 (2000)

  4. 4.

    Cho, S.-Y., Chow, T.W.S.: Robust face recognition By using generalised neural reflectance model. Neural Comput. Appl. J. 15, 170–182 (2006)

    Article  Google Scholar 

  5. 5.

    Chen, H., Bhanu, B.: Human ear recognition in 3D. IEEE Trans. PAMI 29(4), 718–737 (2007)

    Google Scholar 

  6. 6.

    Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: 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 (2001)

    Google Scholar 

  7. 7.

    Healy, G., Binford, T.O.: Local shape from specularity. Comput. Vis. Graph. Image Process. 42, 62–86 (1988)

    Article  Google Scholar 

  8. 8.

    Torrance, K.E., Sparrow, E.M.: Theory for off-specular reflection from roughened surfaces. J. Opt. Soc. Am. 57, 1105–1114 (1967)

    Article  Google Scholar 

  9. 9.

    Nayar, S.K., Ikeuchi, K., Kanade, T.: Determining shape and reflectance of hybrid surfaces by photometric sampling. IEEE Trans. Robotics Autom. 6, 418–430 (1990)

    Article  Google Scholar 

  10. 10.

    Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximation. Neural Netw. 2, 359–366 (1989)

    Google Scholar 

  11. 11.

    Park, J., Sandberg, I.W.: Universal approximation using radial basis function networks. Neural Comput. 3, 261–257 (1991)

    Article  Google Scholar 

  12. 12.

    Cho, S.Y., Chow, T.W.S.: Enhanced 3D shape recovery using the neural-based hybrid reflectance model. Neural Comput. 13, 2617–2637 (2001)

    MATH  Article  Google Scholar 

  13. 13.

    Cho, S.Y., Chow, T.W.S.: Neural computation approach for developing a 3-D shape reconstruction model. IEEE Trans. Neural Netw. 12(5), 1204–1214 (2002)

    Google Scholar 

  14. 14.

    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)

    Article  Google Scholar 

  15. 15.

    Swets, D.L., Weng, J.: ’Using discriminant eigenfeatures for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 18, 831–836 (1996)

    Article  Google Scholar 

  16. 16.

    Islam, S.M.S., Davies, R., Ben-namoun, M., Mian, A.S.: Efficient detection and recognition of 3D ears. Int. J. Comput. Vis. 95, 52–73 (2011)

    Google Scholar 

Download references

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Correspondence to Siu-Yeung Cho.

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Cho, SY. 3D ear shape reconstruction and recognition for biometric applications. SIViP 7, 609–618 (2013). https://doi.org/10.1007/s11760-013-0481-y

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Keywords

  • Ear recognition
  • 3D shape reconstruction
  • Principal component analysis
  • Fisher’s discriminant analysis