Skip to main content
SpringerLink
Log in

Long Period Re-identification Approach to Improving the Quality of Education: A Preliminary Study

  • Conference paper
  • First Online:
Advances in Information and Communication (FICC 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1130))

Included in the following conference series:

  • 1303 Accesses

Abstract

Early school leaving is one of the most frequently mentioned reasons to social exclusion later in life. In order to reduce the risk of early school leaving, it is necessary to automate the process of entering unjustified lessons’ delays in school management system. A person’s re-identifying (Re-ID) is a complex automated process, where most studies use an approach to analyze the descriptors of clothing and appearance that are intended for the use of short-period Re-ID. In contrast, there is not much research in the real-time long-term Re-ID process, when images or videos are taken at intervals of several days or months in an uncontrolled environment. In this case descriptors characterizing a person’s biometric identity based on unique features, such as a facial digital image, are required. The objective of this research is to develop a real-time person’s long-term re-identification approach for accounting of non-attended lessons in educational institutions. The proposed Re-ID mechanism includes face identification and new method of using multiple face etalon versions and multiple versions of descriptors for a single person. This allows Re-ID of a person in different clothing and appearance from different camera angles in a long term.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Nevala, A.M., Hawley, J., Stokes, D., Slater, K., Otero, M.S., Santos, R., Duchemin, C., Manoudi, A.: Reducing early school leaving in the EU. European Parliament, Brussels (2011)

    Google Scholar 

  2. Bedagkar-Gala, A., Shah, S.K.: A survey of approaches and trends in person re-identification. Image Vis. Comput. 32(4), 270–286 (2014)

    Article  Google Scholar 

  3. Lee, K.W., Sankaran, N., Setlur, S., Napp, N., Govindaraju, V.: Wardrobe model for long term re-identification and appearance prediction. In: 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Auckland, New Zealand, pp. 1–6 (2018)

    Google Scholar 

  4. Ding, Y.: Pedestrian re-identification based on image enhancement and over-fitting solution strategies. In: 5th International Conference on Systems and Informatics (ICSAI), Nanjing, China, pp. 745–750. IEEE (2018)

    Google Scholar 

  5. Nambiar, A., Bernardino, A.A.: Context-aware method for view-point invariant long-term re-identification. In: Cláudio, A., et al. (eds.) Computer Vision, Imaging and Computer Graphics – Theory and Applications, VISIGRAPP 2017. Communications in Computer and Information Science, vol. 983, pp. 329–351. Springer, Cham (2017)

    Google Scholar 

  6. Kamalakumari, J., Muthuraman, V.: Recognizing heterogeneous faces-a study. Int. J. Pure Appl. Math. 118(8), 661–663 (2018)

    Google Scholar 

  7. Hassaballah, M., Aly, S.: Face recognition: challenges, achievements, and future directions. IET Comput. Vis. 9(4), 614–626 (2015)

    Article  Google Scholar 

  8. The American Society for Aesthetic Plastic Surgery. https://www.surgery.org/sites/default/files/ASAPS-Stats2018_0.pdf. Accessed 01 June 2019

  9. Kim, Y.A., Cho Chung, H.I.: Side effect experiences of South Korean women in their twenties and thirties after facial plastic surgery. Int. J. Women’s Health 10, 309–316 (2018)

    Article  Google Scholar 

  10. Singh, R., Vatsa, M., Noore, A.: Effect of plastic surgery on face recognition: a preliminary study. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 72–77 (2009)

    Google Scholar 

  11. Mayes, A.E., Murray, P.G., Gunn, D.A., Tomlin, C.C., Catt, S.D., Wen, Y.B., Zhou, L.P., Wang, H.Q., Catt, M., Granger, S.P.: Environmental and lifestyle factors associated with perceived facial age in Chinese women. PLoS ONE 5(12), 1–7 (2010). e15270

    Article  Google Scholar 

  12. Yadav, D., Singh, R., Vatsa, M., Noore, A.: Recognizing age-separated face images: humans and machines. PLoS ONE 9(12), 1–22 (2014). e112234

    Google Scholar 

  13. Wen, D., Fang, C., Ding, X., Zhang, T.: Development of recognition engine for baby faces. In: 20th International Conference on Pattern Recognition, Istanbul, Turkey, pp. 3408–3411 (2010)

    Google Scholar 

  14. Siddiqui, S., Vatsa, M., Singh, R.: Face recognition for newborns, toddlers, and pre-school children: a deep learning approach. In: 24th International Conference on Pattern Recognition (ICPR), pp. 3156–3161. IEEE (2018)

    Google Scholar 

  15. Han, H., Otto, C., Liu, X., Jain, A.K.: Demographic estimation from face images: human vs. machine performance. IEEE Trans. Pattern Anal. Mach. Intell. 37(6), 1148–1161 (2015)

    Article  Google Scholar 

  16. Mandal, B.: Face recognition: perspectives from the real world. In: 14th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 1–5. IEEE (2016)

    Google Scholar 

  17. Rothoft, V., Si, J., Jiang, F., Shen, R.: Monitor pupils’ attention by image super-resolution and anomaly detection. In: International Conference on Computer Systems, Electronics and Control (ICCSEC), pp. 843–847. IEEE (2017)

    Google Scholar 

  18. Hendel, R.K., Starrfelt, R., Gerlach, C.: The good, the bad, and the average: characterizing the relationship between face and object processing across the face recognition spectrum. Neuropsychologia 124, 274–284 (2019)

    Article  Google Scholar 

  19. Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. In: British Machine Vision Conference (BMVC), pp. 41.1–41.12. BMVA Press (2015)

    Google Scholar 

  20. Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: IEEE Conference on Computer Vision and Pattern Recognition, Boston, pp. 815–823 (2015)

    Google Scholar 

  21. Zhuang, N., Zhang, Q., Pan, C., Ni, B., Xu, Y., Yang, X., Zhang, W.: Recognition oriented facial image quality assessment via deep convolutional neural network. Neurocomputing 358, 109–118 (2019)

    Article  Google Scholar 

  22. Heyman, J.: TracTrac: a fast multi-object tracking algorithm for motion estimation. Comput. Geosci. 128, 11–18 (2019)

    Article  Google Scholar 

Download references

Acknowledgments

The research leading to these results has received funding from the project “Competence Centre of Information and Communication Technologies” of EU Structural funds, contract No. 1.2.1.1/18/A/003 signed between IT Competence Centre and Central Finance and Contracting Agency, Research No. 2.1 “Person long-period re-identification (Re-ID) solution to improve the quality of education”.

Author information

Authors and Affiliations

  1. WeAreDots Ltd., Riga, LV 1050, Latvia

    Irina Arhipova, Gatis Vitols & Inga Meirane

Authors
  1. Irina Arhipova
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gatis Vitols
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Inga Meirane
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Irina Arhipova .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Arhipova, I., Vitols, G., Meirane, I. (2020). Long Period Re-identification Approach to Improving the Quality of Education: A Preliminary Study. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication. FICC 2020. Advances in Intelligent Systems and Computing, vol 1130. Springer, Cham. https://doi.org/10.1007/978-3-030-39442-4_14

Download citation

Publish with us

Policies and ethics

Search

Navigation