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

Ear recognition in 3D using 2D curvilinear features

Ear recognition in 3D using 2D curvilinear features

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

Buy eFirst 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 Biometrics — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

This study presents a novel approach for human recognition using co-registered three-dimensional (3D) and 2D ear images. The proposed technique is based on local feature detection and description. The authors detect feature key-points in 2D ear images utilising curvilinear structure and map them to the 3D ear images. Considering a neighbourhood around each mapped key-point in 3D, a feature descriptor vector is computed. To match a probe 3D ear image with a gallery 3D ear image for recognition, first highly similar feature key-points of these images are used as correspondence points for an initial alignment. Afterwards, a fine iterative closest point matching is performed on entire data of the 3D ear images being matched. An extensive experimental analysis is performed to demonstrate the recognition performance of the proposed approach in the presence of noise and occlusions, and compared with the available state-of-the-art 3D ear recognition techniques. The recognition rate of the proposed technique is found to be 98.69% on the University of Notre Dame-Collection J2 dataset with an equal error rate of 1.53%.

References

    1. 1)
      • 1. Iannarelli, A.: ‘Ear identification’ (Paramount Publishing Company, Freemont, CA, 1989).
    2. 2)
      • 2. Jain, A.K., Ross, A., Prabhakar, S.: ‘An introduction to biometric recognition’, IEEE Trans. Circuits Syst. Video Technol., 2004, 14, (1), pp. 420.
    3. 3)
      • 3. Chen, H., Bhanu, B.: ‘Human ear recognition in 3D’, IEEE Trans. Pattern Anal. Mach. Intell., 2007, 29, (4), pp. 718737.
    4. 4)
      • 4. Prakash, S., Gupta, P.: ‘Ear biometrics in 2D and 3D: localization and recognition’ (Springer, Singapore, 2015, 1st edn.).
    5. 5)
      • 5. Kumar, A., Wu, C.: ‘Automated human identification using ear imaging’, Pattern Recognit., 2012, 45, (3), pp. 956968.
    6. 6)
      • 6. Chowdhury, D.P., Bakshi, S., Guo, G., et al: ‘On applicability of tunable filter bank based feature for ear biometrics: a study from constrained to unconstrained’, J. Med. Syst., 2018, 42, (1), p. 11.
    7. 7)
      • 7. Bustard, J.D., Nixon, M.S.: ‘Toward unconstrained ear recognition from two-dimensional images’, IEEE Trans. Syst. Man Cybern. A, Syst. Hum., 2010, 40, (3), pp. 486494.
    8. 8)
      • 8. Emeršič, Ž., Štepec, D., Štruc, V., et al: ‘Training convolutional neural networks with limited training data for ear recognition in the wild’, arXiv preprint arXiv:171109952, 2017.
    9. 9)
      • 9. Zhang, Y., Mu, Z., Yuan, L., et al: ‘Ear verification under uncontrolled conditions with convolutional neural networks’, IET Biometrics, 2018, 7, (3), pp. 185198.
    10. 10)
      • 10. Dodge, S., Mounsef, J., Karam, L.: ‘Unconstrained ear recognition using deep neural networks’, IET Biometrics, 7, (3), 2018, pp. 207214.
    11. 11)
      • 11. Prakash, S., Gupta, P.: ‘Human recognition using 3D ear images’, Neurocomputing, 2014, 140, pp. 317325.
    12. 12)
      • 12. Yan, P., Bowyer, K.Multi-biometrics 2D and 3D ear recognition’. Proc. Audio- and Video-based Biometric Person Authentication, 2005, pp. 459474.
    13. 13)
      • 13. Yan, P., Bowyer, K.W.: ‘Biometric recognition using 3D ear shape’, IEEE Trans. Pattern Anal. Mach. Intell., 2007, 29, (8), pp. 12971308.
    14. 14)
      • 14. Chen, H., Bhanu, B.: ‘Contour matching for 3D ear recognition’. Proc. IEEE Conf. Application of Computer Vision, 2005, vol. 1, pp. 123128.
    15. 15)
      • 15. Passalis, G., Kakadiaris, I.A., Theoharis, T., et al: ‘Towards fast 3D ear recognition for real-life biometric applications’. Proc. IEEE Conf. Advanced Video and Signal based Surveillance, 2007, pp. 3944.
    16. 16)
      • 16. Zhou, J., Cadavid, S., Abdel Mottaleb, M.: ‘A computationally efficient approach to 3D ear recognition employing local and holistic features’. Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshops, 2011, pp. 98105.
    17. 17)
      • 17. Islam, S.M., Davies, R., Bennamoun, M., et al: ‘Multibiometric human recognition using 3D ear and face features’, Pattern Recognit., 2013, 46, (3), pp. 613627.
    18. 18)
      • 18. Islam, S.M., Davies, R., Bennamoun, M., et al: ‘Efficient detection and recognition of 3D ears’, Int. J. Comput. Vis., 2011, 95, (1), pp. 5273.
    19. 19)
      • 19. Guo, Y., Sohel, F., Bennamoun, M., et al: ‘Rotational projection statistics for 3D local surface description and object recognition’, Int. J. Comput. Vis., 2013, 105, (1), pp. 6386.
    20. 20)
      • 20. Besl, P.J., McKay, N.D.: ‘A method for registration of 3-D shapes’, IEEE Trans. Pattern Anal. Mach. Intell., 1992, 14, (2), pp. 239256.
    21. 21)
      • 21. Zhang, Y., Mu, Z., Yuan, L., et al: ‘3D ear normalization and recognition based on local surface variation’, Appl. Sci., 2017, 7, (1), p. 104.
    22. 22)
      • 22. Sun, X., Wang, G., Wang, L., et al: ‘3D ear recognition using local salience and principal manifold’, Graph. Models, 2014, 76, (5), pp. 402412.
    23. 23)
      • 23. Abaza, A., Ross, A., Hebert, C., et al: ‘A survey on ear biometrics’, ACM Comput. Surv., 2013, 45, (2), p. 22.
    24. 24)
      • 24. Emeršič, Ž., Štruc, V., Peer, P.: ‘Ear recognition: more than a survey’, Neurocomputing, 2017, 255, pp. 2639.
    25. 25)
      • 25. Pflug, A., Busch, C.: ‘Ear biometrics: a survey of detection, feature extraction and recognition methods’, IET Biometrics, 2012, 1, (2), pp. 114129.
    26. 26)
      • 26. Chen, H., Bhanu, B.: ‘Efficient recognition of highly similar 3D objects in range images’, IEEE Trans. Pattern Anal. Mach. Intell., 2009, 31, (1), pp. 172179.
    27. 27)
      • 27. Ganapathi, I.I., Prakash, S.: ‘3D ear recognition using global and local features’, IET Biometrics, 2018, 7, (3), pp. 232241.
    28. 28)
      • 28. Ganapathi, I.I., Prakash, S.: ‘False mapped feature removal in spin images based 3D ear recognition’. Proc. Int. Conf. Signal Processing and Integrated Networks, 2016, pp. 620623.
    29. 29)
      • 29. Kass, M., Witkin, A., Terzopoulos, D.: ‘Snakes: active contour models’, Int. J. Comput. Vis., 1988, 1, (4), pp. 321331.
    30. 30)
      • 30. Islam, S., Davies, R., Mian, A., et al: ‘A fast and fully automatic ear recognition approach based on 3D local surface features’. Proc. Advanced Concepts for Intelligent Vision Systems, 2008, pp. 10811092.
    31. 31)
      • 31. Zeng, H., Dong, J.Y., Mu, Z.C., et al: ‘Ear recognition based on 3D keypoint matching’. Proc. IEEE Int. Conf. Signal Processing, 2010, pp. 16941697.
    32. 32)
      • 32. Cadavid, S., Abdel Mottaleb, M.: ‘3D ear modeling and recognition from video sequences using shape from shading’, IEEE Trans. Inf. Forensics Sec., 2008, 3, (4), pp. 709718.
    33. 33)
      • 33. Liu, H., Yan, J.: ‘Multi-view ear shape feature extraction and reconstruction’. Proc. IEEE Conf. Signal-Image Technologies and Image based Systems, 2007, pp. 652658.
    34. 34)
      • 34. Theoharis, T., Passalis, G., Toderici, G., et al: ‘Unified 3D face and ear recognition using wavelets on geometry images’, Pattern Recognit., 2008, 41, (3), pp. 796804.
    35. 35)
      • 35. Li, L., Zhang, L., Li, H.: ‘3D ear identification using LC-KSVD and local histograms of surface types’. Proc. IEEE Int. Conf. Multimedia and Expo, 2015, pp. 16.
    36. 36)
      • 36. Liu, Y., Zhang, B., Zhang, D.: ‘Ear-parotic face angle: a unique feature for 3D ear recognition’, Pattern Recognit. Lett., 2015, 53, pp. 915.
    37. 37)
      • 37. Sun, X.P., Li, S.H., Han, F., et al: ‘3D ear shape matching using joint α-entropy’, J. Comput. Sci. Technol., 2015, 30, (3), pp. 565577.
    38. 38)
      • 38. Panagiotakis, C., Kokinou, E., Sarris, A.: ‘Curvilinear structure enhancement and detection in geophysical images’, IEEE Trans. Geosci. Remote Sens., 2011, 49, (6), pp. 20402048.
    39. 39)
      • 39. Gonzalez, R.C., Woods, R.E.: ‘Digital image processing’ (Prentice-Hall, Inc., 2006, 3rd edn.).
    40. 40)
      • 40. Zhang, Y., Hamza, A.B.: ‘Vertex-based anisotropic smoothing of 3D mesh data’. Proc. IEEE Conf. Electrical and Computer Engineering, 2006, pp. 202205.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2018.5064
Loading

Related content

content/journals/10.1049/iet-bmt.2018.5064
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address