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Facial Recognition on Cloud for Android Based Wearable Devices

  • Zeeshan ShaukatEmail author
  • Chuangbai Xiao
  • M. Saqlain Aslam
  • Qurat ul Ain Farooq
  • Sara Aiman
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 973)

Abstract

Facial recognition applications for Android Based Wearable Devices (ABWD) can benefit from cloud computing as they become easy to acquire and widely available. There are several applications of facial recognition in terms of assistance, guidance, security and so on. We can greatly reduce the processing time by executing the facial recognition application on cloud, and clients will not have to store the big data for the image verification on their local machine (mobile phones, pc’s etc.). Comparing to the cost of acquiring an equally strong server machine, cloud computing increases the storage and processing power with very less cost. In this research plan is to enhance the user experience of augmented display on android based wearable devices, and for doing that, this system is being proposed in which a person wearing Android based smart glasses will send an image of an object to Hadoop (open-source software for scalable, reliable, distributed computing) powered cloud server. Facial Recognition Application on cloud server will recognize the face from already present database on server and then respond results to Android Based Wearable client devices. Then android based wearable smart devices will display the detail result in form of augmented display to the person wearing them. By transferring the process of facial recognition and having the database on cloud server, multiple clients no longer need to maintain their local databases and the device will require less processing power which results in reduction of cost and processing time.

Keywords

Keywords facial recognition Android Wearable devices Cloud computing Augmented reality 

References

  1. 1.
    Lenc, L., Král, P.: Automatic face recognition system based on the SIFT features. Comput. Electr. Eng. 46(Supplement C), 256–272 (2015)CrossRefGoogle Scholar
  2. 2.
    Aminzadeh, N., Sanaei, Z., Ab Hamid, S.H.: Mobile storage augmentation in mobile cloud computing: taxonomy, approaches, and open issues. Simul. Model. Pract. Theor. 50(Supplement C), 96–108 (2015)CrossRefGoogle Scholar
  3. 3.
    Wang, X., et al.: Person-of-interest detection system using cloud-supported computerized-eyewear. In: 2013 IEEE International Conference on Technologies for Homeland Security (HST) (2013)Google Scholar
  4. 4.
    Chaudhry, S., Chandra, R.: Face detection and recognition in an unconstrained environment for mobile visual assistive system. Appl. Soft Comput. 53(Supplement C), 168–180 (2017)CrossRefGoogle Scholar
  5. 5.
    Mann, S., Mann, S.: My Augmediated Life. IEEE Spectrum (2013)Google Scholar
  6. 6.
    Wikipedia. Google Glass. (2018, January 5). In: Wikipedia. 2018; Available from https://en.wikipedia.org/wiki/Google_Glass
  7. 7.
    Rahman, S.A., et al.: Unintrusive eating recognition using Google Glass. In: Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2015 9th International Conference on. IEEE (2015)Google Scholar
  8. 8.
    Lv, Z., et al.: Hand-free motion interaction on google glass. In: SIGGRAPH Asia 2014 Mobile Graphics and Interactive Applications. ACM (2014)Google Scholar
  9. 9.
    Tang, J.: The Mirror API, in Beginning Google Glass Development. Springer, pp. 297–336 (2014)Google Scholar
  10. 10.
    Ha, K., et al.: Towards wearable cognitive assistance. In: Proceedings of the 12th annual international conference on Mobile systems, applications, and services. ACM (2014)Google Scholar
  11. 11.
    Bonsor, K., Johnson, R.: How facial recognition systems work. HowStuffWorks. Com Np (2001)Google Scholar
  12. 12.
    Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cognit. Neurosci. 3(1), 71–86 (1991)CrossRefGoogle Scholar
  13. 13.
    Lee, H.-J., Lee, W.-S., Chung, J.-H.: Face recognition using Fisherface algorithm and elastic graph matching. In: Proceedings of 2001 International Conference on Image Processing, 2001. IEEE (2001)Google Scholar
  14. 14.
    Abate, A.F., et al.: 2D and 3D face recognition: a survey. Pattern Recogn. Lett. 28(14), 1885–1906 (2007)CrossRefGoogle Scholar
  15. 15.
    Kakadiaris, I.A., et al.: Three-dimensional face recognition in the presence of facial expressions: an annotated deformable model approach. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 640–649 (2007)CrossRefGoogle Scholar
  16. 16.
    Baggio, D.L.: Mastering OpenCV with practical computer vision projects. 2012: Packt Publishing LtdGoogle Scholar
  17. 17.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefGoogle Scholar
  18. 18.
    Zhang, B., et al.: Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Trans. Image Process. 19(2), 533–544 (2010)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Karakashev, D.Z., Tan, H.Z.: Exploring How Haptics Contributes to Immersion in Virtual Reality (2016)Google Scholar
  20. 20.
    Juan Fang, Z.S., Ali, S., Zulfiqar, A.A.: Cloud computing: virtual web hosting on infrastructure as a service (IAAS). In: 13th International Conference on Mobile Ad-hoc and Sensor Networks, MSN. Springer (2017)Google Scholar
  21. 21.
    Mollah, M.B., Islam, K.R., Islam, S.S.: Next generation of computing through cloud computing technology. In: Electrical & Computer Engineering (CCECE), 2012 25th IEEE Canadian Conference on. IEEE (2012)Google Scholar
  22. 22.
    Wen, Y., et al.: Forensics-as-a-service (FAAS): computer forensic workflow management and processing using cloud. In: The Fifth International Conferences on Pervasive Patterns and Applications (2013)Google Scholar
  23. 23.
    Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRefGoogle Scholar
  24. 24.
    Ananthanarayanan, R., et al.: Cloud analytics: do we really need to reinvent the storage stack? In: HotCloud (2009)Google Scholar
  25. 25.
    Fuad, A., Erwin, A., Ipung, H.P.: Processing performance on apache pig, apache hive and MySQL cluster. In: Information, Communication Technology and System (ICTS), 2014 International Conference on. IEEE (2014)Google Scholar
  26. 26.
    Xu, G., Xu, F., Ma, H.: Deploying and researching Hadoop in virtual machines. In: Automation and Logistics (ICAL), 2012 IEEE International Conference on. IEEE (2012)Google Scholar
  27. 27.
    Joshi, S.B.: Apache hadoop performance-tuning methodologies and best practices. In: Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering. ACM (2012)Google Scholar
  28. 28.
    Shaukat, Z., Fang, J., Azeem, M., Akhtar, F., Ali, S.: Cloud based face recognition for google glass. In: Proceedings of the 2018 International Conference on Computing and Artificial Intelligence (ICCAI 2018). Association for Computing Machinery, pp. 104–111 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Zeeshan Shaukat
    • 1
    Email author
  • Chuangbai Xiao
    • 1
  • M. Saqlain Aslam
    • 1
  • Qurat ul Ain Farooq
    • 1
  • Sara Aiman
    • 1
  1. 1.Beijing University of TechnologyBeijingChina

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