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Evaluation of Local Texture Descriptors for Eyebrow-Based Continuous Mobile User Authentication

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Applied Computing to Support Industry: Innovation and Technology (ACRIT 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1174))

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Abstract

Mobile user authentication plays an important role in securing physical and logical access, especially to globally ubiquitous smart phones. Several studies have evaluated face, ocular and finger modalities for mobile user authentication. Human eyebrow is among the less explored traits for mobile biometric use cases where device front facing cameras can easily scan them. A handful of studies suggest the potential of human eyebrows for person authentication. Using Histogram of Oriented Gradients (HOG) based texture descriptors, we show equal error rates as low as 15.32% and areas under ROC curve as high as 0.92 on publicly available VISOB dataset when fusing left and right eyebrow units.

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References

  1. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection (2005)

    Google Scholar 

  2. García-Olalla, O., Alegre, E., Ferna´ndez-Robles, L., González-Castro, V.: Local oriented statistics information booster (LOSIB) for texture classification. In: 2014 22nd International Conference on Pattern Recognition, pp. 1114–1119. IEEE (2014)

    Google Scholar 

  3. Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657–1663 (2010)

    Article  MathSciNet  Google Scholar 

  4. He, D.: An efficient remote user authentication and key agreement protocol for mobile client–server environment from pairings. Ad Hoc Netw. 10(6), 1009–1016 (2012)

    Article  Google Scholar 

  5. Juefei-Xu, F., Savvides, M.: Can your eyebrows tell me who you are? In: 2011 5th International Conference on Signal Processing and Communication Systems (ICSPCS), pp. 1–8. IEEE (2011)

    Google Scholar 

  6. Jun-bin, C., Haitao, Y., Lili, D.: Eyebrows identity authentication based on wavelet transform and support vector machines. Phys. Procedia 25, 1337–1341 (2012)

    Article  Google Scholar 

  7. Kannala, J., Rahtu, E.: BSIF: Binarized statistical image features. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR 2012), pp. 1363–1366. IEEE (2012)

    Google Scholar 

  8. Kim, D.S., Hong, K.S.: Multimodal biometric authentication using teeth image and voice in mobile environment. IEEE Trans. Consum. Electron. 54(4), 1790–1797 (2008)

    Article  Google Scholar 

  9. King, D.E.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10(Jul), 1755–1758 (2009)

    Google Scholar 

  10. Kyazimov, T., Makhmudova, S.J.: Information identification system for identifying people by portrait photos. In: The 6th International Scientific and Technical Conference of the Internet—Formation—The Science—2008,” Vinnitsa, pp. 86–89 (2008)

    Google Scholar 

  11. Le, T.H.N., Prabhu, U., Savvides, M.: A novel eyebrow segmentation and eyebrow shape-based identification. In: IEEE International Joint Conference on Biometrics, pp. 1–8. IEEE (2014)

    Google Scholar 

  12. Li, Y., Li, H., Cai, Z.: Human eyebrow recognition in the matching-recognizing framework. Comput. Vis. Image Underst. 117(2), 170–181 (2013)

    Article  Google Scholar 

  13. Li, Y., Li, X.: Hmm based eyebrow recognition. In: Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2007), vol. 1, pp. 135–138. IEEE (2007)

    Google Scholar 

  14. Marsico, M.D., Nappi, M., Riccio, D., Wechsler, H.: Mobile iris challenge evaluation (MICHE)-I, biometric iris dataset and protocols. Pattern Recogn. Lett. 57, 17–23 (2015)

    Article  Google Scholar 

  15. Mehta, R., Egiazarian, K.O.: Rotated local binary pattern (RLBP)-rotation invariant texture descriptor. In: ICPRAM, pp. 497–502 (2013)

    Google Scholar 

  16. Mohammad, A.S., Rattani, A., Derakhshani, R.: Short-term user authentication using eyebrows biometric for smartphone devices. In: IEEE Computer Science and Electronic Engineering Conference, pp. 1–6 (2018)

    Google Scholar 

  17. Mohammad, A.S., Al-Ani, J.A.: Towards ethnicity detection using learning based classifiers. In: 2017 9th Computer Science and Electronic Engineering (CEEC), pp. 219–224. IEEE (2017)

    Google Scholar 

  18. Mohammad, A.S., Al-Ani, J.A.: Convolutional neural network for ethnicity classification using ocular region in mobile environment. In: 2018 10th Computer Science and Electronic Engineering (CEEC), pp. 293–298. IEEE (2018)

    Google Scholar 

  19. Mohammad, A.S., Rattani, A., Derakhshani, R.: Eyeglasses detection based on learning and non-learning based classification schemes. In: 2017 IEEE International Symposium on Technologies for Homeland Security (HST). pp. 1–5. IEEE (2017)

    Google Scholar 

  20. Mohammad, A.S., Rattani, A., Derakhshani, R.: Comparison of squeezed convolutional neural network models for eyeglasses detection in mobile environment. J. Comput. Sci. Coll. 33(5), 136–144 (2018)

    Google Scholar 

  21. Mohammad, A.S., Rattani, A., Derakhshani, R.: Eyebrows and eyeglasses as soft biometrics using deep learning. IET Biometrics 8, 378–390 (2019)

    Article  Google Scholar 

  22. Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  Google Scholar 

  23. Ojansivu, V., Heikkilä, J.: Blur insensitive texture classification using local phase quantization. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2008. LNCS, vol. 5099, pp. 236–243. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69905-7_27

    Chapter  Google Scholar 

  24. Park, U., Ross, A., Jain, A.K.: Periocular biometrics in the visible spectrum: a feasibility study. In: 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–6. IEEE (2009)

    Google Scholar 

  25. Rattani, A., Derakhshani, R.: Ocular biometrics in the visible spectrum: a survey. Image and Vis. Comput. 59, 1–16 (2017)

    Article  Google Scholar 

  26. Rattani, A., Derakhshani, R.: On fine-tuning convolutional neural networks for smartphone based ocular recognition. In: IEEE International Joint Conference on Biometrics (IJCB), pp. 762–767, October 2017

    Google Scholar 

  27. Rattani, A., Derakhshani, R.: Online co-training in mobile ocular biometric recognition. In: IEEE International Symposium on Technologies for Homeland Security (HST), pp. 1–5, April 2017

    Google Scholar 

  28. Rattani, A., Derakhshani, R.: A survey of mobile face biometrics. Comput. Electr. Eng. 72, 39–52 (2018)

    Article  Google Scholar 

  29. Rattani, A., Derakhshani, R., Ross, A.: Selfie Biometrics: Advances and Challenges, 1st edn. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26972-2

    Book  Google Scholar 

  30. Rattani, A., Reddy, N., Derakhshani, R.: Convolutional neural network for age classification from smart-phone based ocular images. In: 2017 IEEE International Joint Conference on Biometrics (IJCB), pp. 756–761, October 2017

    Google Scholar 

  31. Rattani, A., Reddy, N., Derakhshani, R.: Gender prediction from mobile ocular images: a feasibility study. In: IEEE Symposium on Technologies for Homeland Security, Waltham, MA, pp. 1–6 (2017)

    Google Scholar 

  32. Rattani, A., Reddy, N., Derakhshani, R.: Convolutional neural networks for gender prediction from smartphone-based ocular images. IET Biometrics 7, 423–430 (2018)

    Article  Google Scholar 

  33. Rattani, A., Derakhshani, R., Saripalle, S.K., Gottemukkula, V.: ICIP 2016 competition on mobile ocular biometric recognition. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 320–324. IEEE (2016)

    Google Scholar 

  34. Ren, J., Jiang, X., Yuan, J.: Relaxed local ternary pattern for face recognition. In: 2013 IEEE International Conference on Image Processing, pp. 3680–3684. IEEE (2013)

    Google Scholar 

  35. Vu, N.S., Caplier, A.: Face recognition with patterns of oriented edge magnitudes. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 313–326. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15549-9_23

    Chapter  Google Scholar 

  36. Yang, X., Xu, X., Liu, C.: Eyebrow recognition based on sparsity preserving projections. In: IEEE Conference Anthology, pp. 1–4. IEEE (2013)

    Google Scholar 

  37. Yujian, L., Cuihua, F.: Eyebrow recognition: a new biometric technique. In: Proceedings of the 9th IASTED International Conference on Signal and Image Processing, pp. 506–510 (2007)

    Google Scholar 

  38. Ziegler, A., Christiansen, E., Kriegman, D., Belongie, S.J.: Locally uniform comparison image descriptor. In: Advances in Neural Information Processing Systems, pp. 1–9 (2012)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, College of Engineering, Mustansiriyah University, Baghdad, Iraq

    Ahmad Saeed Mohammad

  2. Department of Electrical Engineering and Computer Science, Wichita State University, Wichita, KS, USA

    Ajita Rattani

  3. Department of Computer Science and Electrical Engineering, University of Missouri-Kansas City, Kansas City, MO, USA

    Reza Derakhshani

Authors
  1. Ahmad Saeed Mohammad
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  2. Ajita Rattani
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  3. Reza Derakhshani
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Corresponding author

Correspondence to Ahmad Saeed Mohammad .

Editor information

Editors and Affiliations

  1. Al Maaref University College, Ramadi, Iraq

    Mohammed I. Khalaf

  2. Liverpool John Moores University, Liverpool, UK

    Dhiya Al-Jumeily

  3. University of Liverpool, Liverpool, UK

    Alexei Lisitsa

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Mohammad, A.S., Rattani, A., Derakhshani, R. (2020). Evaluation of Local Texture Descriptors for Eyebrow-Based Continuous Mobile User Authentication. In: Khalaf, M., Al-Jumeily, D., Lisitsa, A. (eds) Applied Computing to Support Industry: Innovation and Technology. ACRIT 2019. Communications in Computer and Information Science, vol 1174. Springer, Cham. https://doi.org/10.1007/978-3-030-38752-5_11

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  • DOI: https://doi.org/10.1007/978-3-030-38752-5_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-38751-8

  • Online ISBN: 978-3-030-38752-5

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