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

Automatic ear detection and feature extraction using Geometric Morphometrics and convolutional neural networks

Automatic ear detection and feature extraction using Geometric Morphometrics and convolutional neural networks

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 Biometrics — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Accurate gathering of phenotypic information is a key aspect in several subject matters, including biometrics, biomedical analysis, forensics, and many other. Automatic identification of anatomical structures of biometric interest, such as fingerprints, iris patterns, or facial traits, are extensively used in applications like access control and anthropological research, all having in common the drawback of requiring intrusive means for acquiring the required information. In this regard, the ear structure has multiple advantages. Not only the ear's biometric markers can be easily captured from the distance with non intrusive methods, but also they experiment almost no changes over time, and are not influenced by facial expressions. Here we present a new method based on Geometric Morphometrics and Deep Learning for automatic ear detection and feature extraction in the form of landmarks. A convolutional neural network was trained with a set of manually landmarked examples. The network is able to provide morphometric landmarks on ears' images automatically, with a performance that matches human landmarking. The feasibility of using ear landmarks as feature vectors opens a novel spectrum of biometrics applications.

References

    1. 1)
      • 1. Paternoster, L., Zhurov, A.I., Toma, A.M., et al: ‘Genome-wide association study of three-dimensional facial morphology identifies a variant in PAX3 associated with nasion position’, Am. J. Hum. Genet., 2012, 90, pp. 478485.
    2. 2)
      • 2. Liu, F., van der Lijn, F., Schurmann, C., et al: ‘A Genome-Wide Association Study Identifies Five Loci Influencing Facial Morphology in Europeans’, PLoS Genet., 2012, 8, (9): e1002932. doi: 10.1371/journal.pgen.1002932.
    3. 3)
      • 3. Gómez-Valdés, J.A., Hünemeier, T., Contini, V., et al: ‘Fibroblast growth factor receptor 1 (FGFR1) variants and craniofacial variation in Amerindians and related populations’, Am. J. Human Biol, 2013, 25, pp. 1219. http://www.ncbi.nlm.nih.gov/pubmed/23070782.
    4. 4)
      • 4. Alberink, I., Ruifrok, A.: ‘Performance of the FearID earprint identification system’, Forensic Sci. Int., 2007, 166, (2–3), pp. 145154. http://www.ncbi.nlm.nih.gov/pubmed/16772109/23/11/16.
    5. 5)
      • 5. Sforza, C., Grandi, G., Binelli, M., et al: ‘Age- and sex-related changes in the normal human ear’, Forensic Sci. Int., 2009, 187, (1–3), p. 110.e17. http://www.ncbi.nlm.nih.gov/pubmed/19356871.
    6. 6)
      • 6. Ibrahim, M.I.S., Nixon, M.S., Mahmoodi, S.: ‘The effect of time on ear biometrics’. 2011 International Joint Conf. on Biometrics (IJCB). IEEE, October 2011, pp. 16. http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=6117584.
    7. 7)
      • 7. Zelditch, M.L., Swiderski, D.L., Sheets, H.D., et al: ‘Geometric morphometrics for biologists’, Elsevier, 2004, 59, (3), p. 457. http://www.sciencedirect.com/science/article/B848M-4MWYDVJ-7/2/2ec7a8306777645c5716a53774ec699c$\delimiter‘026E30F$nhttp://books.google.com/books?id=LKCVAGn8vkoC{&}pgis=1.
    8. 8)
      • 8. Pflug, S., Busch, C.: ‘Ear biometrics: a survey of detection, feature extraction and recognition methods’, IET Biometrics, 2012, 1, (2), p. 114.
    9. 9)
      • 9. Iannarelli, A.V.: ‘Ear identification’ (Paramont Publishing Company, 1989), vol. 1. https://books.google.com/books?id=jgPkAAAACAAJ{&}pgis=1.
    10. 10)
      • 10. Chen, H., Bhanu, B.: ‘Shape model-based 3D ear detection from side face range images’. 2005 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR'05) – Workshops. IEEE, vol. 3, pp. 122122. http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=1565434.
    11. 11)
      • 11. Chen, H., Bhanu, B.: ‘Contour matching for 3D ear recognition’. Seventh IEEE Workshop on Applications of Computer Vision, WACV 2005, 2007, pp. 123128.
    12. 12)
      • 12. Attarchi, S., Faez, K., Rafiei, A.: Advanced Concepts for Intelligent Vision Systems, October 2008 (LNCS, 5259). http://dl.acm.org/citation.cfm?id=1462298.1462399.
    13. 13)
      • 13. Ansari, S., Gupta, P.: ‘Localization of ear using outer helix curve of the ear’. International Conf. on Computing: the Theory and Applications, 2007, pp. 688692.
    14. 14)
      • 14. Prakash, S., Gupta, P.: ‘An efficient ear localization technique’, Image Vis. Comput., 2012, 30, (1), pp. 3850.
    15. 15)
      • 15. Yan, P., Bowyer, K.W.: ‘Biometric recognition using 3D ear shape’, IEEE Trans. Pattern Anal. Mach. Intell., 2007, 29, (8), pp. 12971308. http://www.ncbi.nlm.nih.gov/pubmed/17568136.
    16. 16)
      • 16. Purkait, R., Singh, P.: ‘A test of individuality of human external ear pattern: its application in the field of personal identification’, Forensic Sci. Int., 2008, 178, (2–3), pp. 112118.
    17. 17)
      • 17. Ercan, I., Ozdemir, S.T., Etoz, A., et al: ‘Facial asymmetry in young healthy subjects evaluated by statistical shape analysis’, J. Anat., 2008, 213, (6), pp. 663669.
    18. 18)
      • 18. Pflug, A., Winterstein, A., Busch, C.: ‘Robust localization of ears by feature level fusion and context information’. Biometrics (ICB), 2013 International Conf. on. IEEE, 2013, pp. 18.
    19. 19)
      • 19. Liu, Y., Zhang, B., Zhang, D.: ‘Ear-parotic face angle: a unique feature for 3D ear recognition’, Pattern Recognit. Lett., 2015, 53, pp. 915. http://linkinghub.elsevier.com/retrieve/pii/S0167865514003316.
    20. 20)
      • 20. Sibai, F.N., Nuaimi, A., Maamari, A., et al: ‘Ear recognition with feed-forward artificial neural networks’, Neural Comput. Appl., 2013, 23, (5), pp. 12651273. http://link.springer.com/10.1007/s00521-012-1068-1.
    21. 21)
      • 21. Abaza, A., Hebert, C., Harrison, M.A.F.: ‘Fast learning ear detection for real-time surveillance’. IEEE 4th International Conf. on Biometrics: Theory, Applications and Systems, BTAS 2010, 2010.
    22. 22)
      • 22. Islam, S.M.S., Bennamoun, M., Davies, R.: ‘Fast and fully automatic ear detection using cascaded adaboost’. 2008 IEEE Workshop on Applications of Computer Vision, WACV, 2008.
    23. 23)
      • 23. Yuan, L., Liu, W., Li, Y.: ‘Non-negative dictionary based sparse representation classification for ear recognition with occlusion’, Neurocomputing, 2016, 171, pp. 540550.
    24. 24)
      • 24. Kumar, A., Chan, T.-S.T.: ‘Robust ear identification using sparse representation of local texture descriptors’, Pattern Recogn., 2013, 46, (1), pp. 7385.
    25. 25)
      • 25. Kumar, A., Wu, C.: ‘Automated human identification using ear imaging’, Pattern Recogn., 2012, 45, (3), pp. 956968.
    26. 26)
      • 26. Chan, T.-S., Kumar, A.: ‘Reliable ear identification using 2-d quadrature filters’, Pattern Recognit. Lett., 2012, 33, (14), pp. 18701881.
    27. 27)
      • 27. Mitteroecker, P., Gunz, P.: Evol Biol, 2009, 36: 235. doi:10.1007/s11692-009-9055-x.
    28. 28)
      • 28. Slice, D.: ‘Modern morphometrics’. Modern morphometrics in physical anthropology, 2005, pp. 146. http://link.springer.com/chapter/10.1007/0-387-27614-9{_}1.
    29. 29)
      • 29. Azaria, R., Adler, N., Silfen, R., et al: ‘Morphometry of the adult human earlobe: a study of 547 subjects and clinical application’, Plast. Reconstr. Surg., 2003, 111, (7), pp. 23982402; discussion 2403-4. http://www.ncbi.nlm.nih.gov/pubmed/12794488.
    30. 30)
      • 30. Alexander, K.S., Stott, D.J., Sivakumar, B., et al: ‘A morphometric study of the human ear’, J. Plastic Reconstruct. Aesthetic Surgery, 2011, 64, (1), pp. 4147. http://www.ncbi.nlm.nih.gov/pubmed/20447883.
    31. 31)
      • 31. Adhikari, K., Reales, G., Smith, A.J.P., et al: ‘A genome-wide association study identifies multiple loci for variation in human ear morphology’, Nat. Commun., 2015, 6, (May), p. 7500. http://www.nature.com/doifinder/10.1038/ncomms8500.
    32. 32)
      • 32. Goodall, C.: ‘Procrustes methods in the statistical analysis of shape’, J. R. Stat. Soc. B (Methodological), 1991, 53, pp. 285339. http://www.jstor.org/stable/2345744.
    33. 33)
      • 33. Ciodaro, T., Deva, D., de Seixas, J.M., et al: ‘Online particle detection with neural networks based on topological calorimetry information’, J. Phys. Conf. Ser., 2012, 368, (1), p. 012030. http://iopscience.iop.org/article/10.1088/1742-6596/368/1/012030.
    34. 34)
      • 34. Ma, J., Sheridan, R.P., Liaw, A., et al: ‘Deep neural nets as a method for quantitative structureactivity relationships’, J. Chem. Inf. Model., 2015, 55, (2), pp. 263274. http://www.ncbi.nlm.nih.gov/pubmed/25635324.
    35. 35)
      • 35. Taigman, Y., Yang, M., Ranzato, M., et al: ‘DeepFace: closing the gap to human-level performance in face verification’. Conf. on Computer Vision and Pattern Recognition (CVPR), 2014, p. 8. http://www.cs.tau.ac.il/{∼}wolf/papers/deepface{_}11{_}01{_}2013.pdf.
    36. 36)
      • 36. LeCun, Y., Bengio, Y., Hinton, G.: ‘Deep learning’, Nature, 2015, 521, (7553), pp. 436444. http://dx.doi.org/10.1038/nature14539$\delimiter‘026E30F$n10.1038/nature14539.
    37. 37)
      • 37. Dieleman, S., Willett, K.W., Dambre, J.: ‘Rotation-invariant convolutional neural networks for galaxy morphology prediction’, Mon. Not. R. Astron. Soc., 2015, 450, (2), pp. 14411459. http://arxiv.org/abs/1503.07077.
    38. 38)
      • 38. Fukushima, K.: ‘Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position’, Biol. Cybern., 1980, 36, (4), pp. 193202.
    39. 39)
      • 39. LeCun, Y., Bottou, L., Bengio, Y., et al: ‘Gradient-based learning applied to document recognition’, Proc. IEEE, 1998, 86, (11), pp. 22782323.
    40. 40)
      • 40. Toshev, A., Szegedy, C.: ‘DeepPose: human pose estimation via deep neural networks’. Computer Vision and Pattern Recognition (CVPR), 2014, pp. 16531660. http://arxiv.org/abs/1312.4659.
    41. 41)
      • 41. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ‘ImageNet classification with deep convolutional neural networks’. Advances in Neural Information Processing Systems, 2012, pp. 19.
    42. 42)
      • 42. Boureau, Y.-L., Ponce, J., Lecun, Y.: ‘A theoretical analysis of feature pooling in visual recognition’. Proc. of the 27th International Conf. on Machine Learning (2010), 2010, pp. 111118. http://www.ece.duke.edu/{∼}lcarin/icml2010b.pdf.
    43. 43)
      • 43. Ruiz-Linares, A., Adhikari, K., Acuña-Alonzo, V., et al: ‘Admixture in Latin America: geographic structure, phenotypic diversity and self-perception of ancestry based on 7,342 individuals’, PLoS Genet., 2014, 10, (9), p. e1004572. http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1004572.
    44. 44)
      • 44. Viola, P., Jones, M.: ‘Robust real-time object detection’, Int. J. Comput. Vis., 2001, 57, pp. 137154. http://scholar.google.com/scholar?hl=en{&}btnG=Search{&}q=intitle:Robust+Real-time+Object+Detection{#}0.
    45. 45)
      • 45. Dieleman, S., Schlüter, J., Raffel, C., et al: ‘Lasagne: first release’, August 2015. http://dx.doi.org/10.5281/zenodo.27878.
    46. 46)
      • 46. Hinton, G.E., Srivastava, N., Krizhevsky, A., et al: ‘Improving neural networks by preventing co-adaptation of feature detectors’, arXiv: 1207.0580, 2012, pp. 118. http://arxiv.org/abs/1207.0580.
    47. 47)
      • 47. Çeliktutan, O., Ulukaya, S., Sankur, B., et al: ‘A comparative study of face landmarking techniques’, EURASIP J. Image and Video Process., 2013, 2013, (1), p. 13. http://jivp.eurasipjournals.springeropen.com/articles/10.1186/1687-5281-2013-13.
    48. 48)
      • 48. Pedregosa, F., Varoquaux, G., Gramfort, A., et al: ‘Scikit-learn: machine learning in Python’, J. Mach. Learn. Res., 2011, 12, pp. 28252830.
    49. 49)
      • 49. Solina, F., Peer, P., Batagelj, B., et al: ‘Color-based face detection in the’ 15 seconds of fame’ art installation’. Proc. of Mirage 2003, Conf. on Computer Vision/Computer Graphics, 2003, pp. 3847.
    50. 50)
      • 50. Geurts, P., Ernst, D., Wehenkel, L.: ‘Extremely randomized trees’, Mach. Learn., 2006, 63, (1), pp. 342.
    51. 51)
      • 51. Wehenkel, L., Ernst, D., Geurts, P.: ‘Ensembles of extremely randomized trees and some generic applications’. Robust Methods for Power System State Estimation and Load Forecasting, 2006.
    52. 52)
      • 52. Faris, C.: ‘Scott-Brown's otorhinolaryngology, head and neck surgery, 7th edn’, October 2011, p. 559. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3604940/.
    53. 53)
      • 53. Cox, T.C., Camci, E.D., Vora, S., et al: ‘The genetics of auricular development and malformation: new findings in model systems driving future directions for microtia research’, Eur. J. Med. Genet., 2014, 57, (8), pp. 394401. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4143470{&}tool=pmcentrez{&}rendertype=abstract.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2016.0002
Loading

Related content

content/journals/10.1049/iet-bmt.2016.0002
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address