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Ear Recognition Based on Gabor-SIFT

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Artificial Intelligence and Security (ICAIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12239))

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Abstract

Scale invariant feature transform is a local point features extraction method. It can find those feature vectors in different scale space which are invariant for scale changes and rotations, and are flexible for illumination variations and affine transformations. The paper chooses SIFT to extract key points of ear images. Then the features of key points are extracted with the local multi-scale analysis feature of the Gabor wavelet. In this way, every key point is represented by a series of multi-scale and multi-orientation Gabor filter coefficients. Finally Ear recognition based on these feature is carried out with Euclidean distance as similarity measurement. Experimental results show that proposed method can effectively extract ear feature points, and obtain high recognition rate by using few feature points. It is robust to rigid changes, illumination and rotations changes of ear image, provides a new approach to the research for ear recognition.

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References

  1. Iannarelli, A.: Ear Identification. Forensic Identification Series. Paramount Publishing Company, California (1989)

    Google Scholar 

  2. Wyawahare, M.V., Patil, P.M., Abhyankar, H.K.: Image registration techniques: an overview. Int. J. Sig. Process. Image Process. Pattern Recogn. 2(3), 11–28 (2009)

    Google Scholar 

  3. Li, Y., Cao, J., Zhang, H.: Improved method of ear recognition based on tensor PCA. Comput. Eng. Appl. 25(47), 171–174 (2011)

    Google Scholar 

  4. Saraswathy, K., Vaithiyanathan, D., Seshasayanan, R.: A DCT approximation with low complexity for image compression. In: Proceedings of 2013 International Conference on Communications and Signal Processing, Melmaruvathur, India, 3–5 April, pp. 465–468 (2013)

    Google Scholar 

  5. Lu, X.-L., Shen, H,-F., Zhao, L.-H.: Ear recognition based on 2DLDA and FSVM. Sci. Technol. Eng. 12, 2852–2855 (2012)

    Google Scholar 

  6. Wu, H., Liu, Q., Liu, X.: A review on deep learning approaches to image classification and object segmentation. Comput. Mater. Continua 60(2), 575–597 (2019)

    Article  Google Scholar 

  7. Yuan, W., Tian, Y.: Ear contour detection based on edge tracking. In: Proceedings of Intelligent Control and Automation (WCICA 2006), Dalian, China, 21–23 June, pp. 10450–10453 (2006)

    Google Scholar 

  8. Mu, Z., Li, Y., Xu, Z., Xi, D., Qi, S.: Shape and structural feature based ear recognition. In: Proceedings of 5th Chinese Conference on Biometric Recognition, Guangzhou, China, 13–14 December, pp. 633–670 (2005)

    Google Scholar 

  9. Li, S., Liu, F., Liang, J., Cai, Z., Liang, Z.: Optimization of face recognition system based on azure IoT edge. Comput. Mater. Continua 61(3), 1377–1389 (2019)

    Article  Google Scholar 

  10. Wang, C., Feng, Y., Li, T., Xie, H., Kwon, G.-R.: A new encryption-then-compression scheme on gray images using the markov random field. Comput. Mater. Continua 56(1), 107–121 (2018)

    Google Scholar 

  11. Daugman, J.G.: Complete discrete 2-D gabor transforms by neural networks for image analysis and compression. IEEE Trans. Acoustics Speech Sig. Process. 7(36), 1169–1179 (1988)

    Article  Google Scholar 

  12. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 2(60), 91–110 (2004)

    Article  Google Scholar 

  13. Wen, W.: An improved algorithm for Harris multi-scale color detection. J. Chongqing Univ. Technol. (Natural Sci.) 8(26), 94–96 (2012)

    Google Scholar 

  14. Steger, C.: An unbiased detector of curvilinear structures. IEEE Trans. Pattern Anal. Mach. Intell. 3(20), 113–125 (1998)

    Article  Google Scholar 

  15. Dakshina, R.K., Hunny, M.: SIFT-based ear recognition by fusion of detected keypoints from color similarity slice regions. In: Proceedings of Advances in Computational Tools for Engineering Applications, Zouk, Mosbeh, 17–19 July (2009)

    Google Scholar 

  16. Tian, Y., Yuan, W.: Ear recognition based on fusion of scale invariant feature transform and geometric feature. Acta Optica Sinica 8(28), 1485–1491 (2008)

    Article  Google Scholar 

  17. Mo, W., Ding, Z.: A novel template weighted match degree algorithm for optical character recognition. Int. J. Smart Home 7(3), 261–270 (2013)

    Google Scholar 

  18. McConnon, G., Deravi, F., Hoque, S., Sirlantzis, K., Howells, G.: An investigation of quality aspects of noisy colour images for Iris recognition. Int. J. Sig. Process. Image Process. Pattern Recogn. 4(3), 165–178 (2011)

    Google Scholar 

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Acknowledgments

Thanks for the image library of Ear Recognition Laboratory at USTB.

This research was supported by (1) Foundation of Liaoning Educational Committee (Grant number 2019LNJC03); (2) Foundation of University of Science and Technology Liaoning (Grant number 2016RC06).

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Authors and Affiliations

  1. School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China

    Ying Tian, Huiwen Dong & Libing Wang

Authors
  1. Ying Tian
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  2. Huiwen Dong
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  3. Libing Wang
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Corresponding author

Correspondence to Ying Tian .

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Editors and Affiliations

  1. Nanjing University of Information Science, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science, Nanjing, China

    Jinwei Wang

  3. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Tian, Y., Dong, H., Wang, L. (2020). Ear Recognition Based on Gabor-SIFT. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Lecture Notes in Computer Science(), vol 12239. Springer, Cham. https://doi.org/10.1007/978-3-030-57884-8_8

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  • DOI: https://doi.org/10.1007/978-3-030-57884-8_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-57883-1

  • Online ISBN: 978-3-030-57884-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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