Skip to main content
SpringerLink
Log in

Long-Term Person Reidentification: Challenges and Outlook

  • Conference paper
  • First Online:
Optimization, Learning Algorithms and Applications (OL2A 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1754))

  • 718 Accesses

Abstract

Person reidentification, i.e., retrieving a person of interest across several non-overlapping cameras, is a task that is far from trivial. Despite its great commercial value and wide range of applications (e.g., surveillance, intelligent environments, forensics, service robotics, marketing), it remains unsolved, even when the individuals do not change clothes during the recognition period. This paper provides an outlook on long-term person reidentification, an emerging research topic regarding when consecutive acquisitions of an individual can be found apart for days or even months, making such a task even more challenging. A long-term reidentification system using face recognition is presented to emphasize current techniques’ limitations.

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 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
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. Baabou, S., et al.: A study of person re-identification system. Am. J. Eng. Rese. (AJER) 7, 210–218 (2018)

    Google Scholar 

  2. Bansal, A., et al.: The do’s and don’ts for CNN-based face verification. In: Proceedings of IEEE International Conference on Computer Vision Workshops (2017)

    Google Scholar 

  3. Barbosa, I.B., et al.: Looking beyond appearances: synthetic training data for deep CNNs in re-identification. Comput. Vis. Image Understand. 167, 50–62 (2018)

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. Bird, N.D., et al.: Detection of loitering individuals in public transportation areas. IEEE Trans. Intell. Transp. Syst. 6, 167–177 (2005)

    Article  Google Scholar 

  6. Bobeldyk, D., Ross, A.: Predicting soft biometric attributes from 30 pixels: a case study in NIR ocular images. In: IEEE Winter Applications of Computer Vision Workshops (2019)

    Google Scholar 

  7. Bondi, E., Pala, P., Seidenari, L., Berretti, S., Del Bimbo, A.: Long term person re-identification from depth cameras using facial and skeleton data. In: Wannous, H., Pala, P., Daoudi, M., Flórez-Revuelta, F. (eds.) UHA3DS 2016. LNCS, vol. 10188, pp. 29–41. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91863-1_3

    Chapter  Google Scholar 

  8. Bucak, S.S., et al.: Efficient multi-label ranking for multi-class learning: Application to object recognition. In: IEEE International Conference on Computer Vision (2009)

    Google Scholar 

  9. Cao, Q., et al.: VGGFace2: a dataset for recognising faces across pose and age. In: Proceedings of 13th IEEE International Conference on Automatic Face and Gesture Recognition (2018)

    Google Scholar 

  10. Dollár, P., et al.: Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 36, 1532–1545 (2014)

    Article  Google Scholar 

  11. Esler, T.: Face recognition using PyTorch (2020)

    Google Scholar 

  12. Geng, X., et al.: Individual stable space: an approach to face recognition under uncontrolled conditions. IEEE Trans. Neural Netw. 19, 1354–1368 (2008)

    Article  Google Scholar 

  13. Gheissari, N., et al.: Person reidentification using spatiotemporal appearance. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2006)

    Google Scholar 

  14. Gou, M., et al.: Person re-identification in appearance impaired scenarios. In: Proceedings of the British Machine Vision Conference 2016 (2016)

    Google Scholar 

  15. Gray, D., Tao, H.: Viewpoint invariant pedestrian recognition with an ensemble of localized features. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5302, pp. 262–275. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88682-2_21

    Chapter  Google Scholar 

  16. Han, F., et al.: Space-time representation of people based on 3D skeletal data: a review. Comput. Vis. Image Underst. 158, 85–105 (2017)

    Article  Google Scholar 

  17. Har-Peled, S., et al.: Constraint classification for multiclass classification and ranking. In: Advances in Neural Information Processing Systems (2002)

    Google Scholar 

  18. Hasan, M., Babaguchi, N.: Long-term people reidentification using anthropometric signature. In: IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS) (2016)

    Google Scholar 

  19. Hirzer, M., Beleznai, C., Roth, P.M., Bischof, H.: Person re-identification by descriptive and discriminative classification. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 91–102. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21227-7_9

    Chapter  Google Scholar 

  20. Hoang, V.T., et al.: 3-D facial landmarks detection for intelligent video systems. IEEE Trans. Ind. Inform. 17, 578–586 (2021)

    Article  Google Scholar 

  21. Hofmann, M., et al.: The TUM Gait from Audio, Image and Depth (GAID) database: multimodal recognition of subjects and traits. J. Vis. Commun. Image Represent. 25, 195–206 (2014)

    Article  Google Scholar 

  22. Hu, C.H., et al.: Face illumination recovery for the deep learning feature under severe illumination variations. Pattern Recogn. 111, 107724 (2021)

    Google Scholar 

  23. Huang, Y., et al.: Celebrities-ReID: a benchmark for clothes variation in long-term person re-identification. In: Proceedings of the International Joint Conference on Neural Networks (2019)

    Google Scholar 

  24. Jayaraman, U., et al.: Recent development in face recognition. Neurocomputing 408, 231–245 (2020)

    Article  Google Scholar 

  25. Karanam, S.: A systematic evaluation and benchmark for person re-identification: features, metrics, and datasets. IEEE Trans. Pattern Anal. Mach. Intell. 41, 523–536 (2019)

    Article  Google Scholar 

  26. Köstinger, M., et al.: Large scale metric learning from equivalence constraints. In: IEEE Conference on Computer Vision and Pattern Recognition (2012)

    Google Scholar 

  27. Krizhevsky, A., et al.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (2012)

    Google Scholar 

  28. LeCun, Y., Bengio, Y.: Convolutional networks for images, speech, and time-series. In: Handbook of Brain Theory and Neural Networks (1998)

    Google Scholar 

  29. Li, W., Wang, X.: Locally Aligned Feature Transforms across Views. In: IEEE Conference on Computer Vision and Pattern Recognition (2013)

    Google Scholar 

  30. Liu, Z., et al.: Person reidentification by joint local distance metric and feature transformation. IEEE Trans. Neural Netw. Learn. Syst. 30, 2999–3009 (2019)

    Article  Google Scholar 

  31. Lu, J., et al.: Learning compact binary face descriptor for face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37, 2041–2056 (2015)

    Article  Google Scholar 

  32. Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of the 7th International Joint Conference on Artificial Intelligence (1981)

    Google Scholar 

  33. Martinel, N., et al.: Person reidentification in a distributed camera network framework. IEEE Trans. Cybern. 47, 3530–3541 (2017)

    Article  Google Scholar 

  34. Munaro, M., et al.: 3D reconstruction of freely moving persons for re-identification with a depth sensor. In: 2014 IEEE International Conference on Robotics and Automation (ICRA) (2014)

    Google Scholar 

  35. Pala, P., et al.: Enhanced skeleton and face 3D data for person re-identification from depth cameras. Comput. Graph. 79, 69–80 (2019)

    Article  Google Scholar 

  36. Plichoski, G.F., et al.: A face recognition framework based on a pool of techniques and differential evolution. Inf. Sci. 543, 219–224 (2021)

    Article  Google Scholar 

  37. Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006). https://doi.org/10.1007/11744023_34

    Chapter  Google Scholar 

  38. Sang, X., et al.: Nonconvex regularizer and latent pattern based robust regression for face recognition. Inf. Sci. 547, 384–403 (2021)

    Article  MathSciNet  Google Scholar 

  39. Satta, R.: Appearance descriptors for person re-identification: a comprehensive review. arXIv (2013)

    Google Scholar 

  40. Schroff, F., et al.: FaceNet: a unified embedding for face recognition and clustering. In: IEEE Conference on Computer Vision and Pattern Recognition (2015)

    Google Scholar 

  41. Schumann, A., Monari, E.: A soft-biometrics dataset for person tracking and re-identification. 11th IEEE International Conference on Advanced Video and Signal-Based Surveillance (2014)

    Google Scholar 

  42. Seide, F., et al.: Feature engineering in Context-Dependent Deep Neural Networks for conversational speech transcription. In: IEEE Workshop on Automatic Speech Recognition and Understanding (2011)

    Google Scholar 

  43. Shi, J., Tomasi, C.: Good features to track. In: IEEE Conference on Computer Vision and Pattern Recognition (1994)

    Google Scholar 

  44. Sun, Y., et al.: Deep convolutional network cascade for facial point detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2013)

    Google Scholar 

  45. Szomszor, M., et al.: Identifying research fronts in the web of science: from metrics to meaning. Technical report, Institute for Scientific Information (2020)

    Google Scholar 

  46. Taghipour-Gorjikolaie, M., et al.: Deep adaptive feature enrichment. Expert Syst. Appl. 162, 113780 (2020)

    Google Scholar 

  47. Tao, D., et al.: Person reidentification by minimum classification error-based KISS metric learning. IEEE Trans. Cybern. 45, 242–252 (2015)

    Article  Google Scholar 

  48. Tomasi, C., Kanade, T.: Detection and tracking of point features. Technical report, International Journal of Computer Vision (1991)

    Google Scholar 

  49. Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57, 137–154 (2004)

    Article  Google Scholar 

  50. Wang, T., et al.: Person re-identification by video ranking (2014)

    Google Scholar 

  51. Wu, D., et al.: Deep learning-based methods for person re-identification: a comprehensive review. Neurocomputing 337, 354–371 (2019)

    Article  Google Scholar 

  52. Wu, F., et al.: Spectrum-aware discriminative deep feature learning for multi-spectral face recognition. Pattern Recogn. 111, 107632 (2021)

    Google Scholar 

  53. Yan, H., Song, C.: Multi-scale deep relational reasoning for facial kinship verification. Pattern Recogn. 110, 107541 (2021)

    Google Scholar 

  54. Zafeiriou, S., et al.: A survey on face detection in the wild: past, present and future. Comput. Vis. Image Underst. 138, 1–24 (2015)

    Article  Google Scholar 

  55. Zhang, K., et al.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23, 1499–1503 (2016)

    Article  Google Scholar 

  56. Zhang, P., et al.: Long-term person re-identification using true motion from videos. In: IEEE Winter Conference on Applications of Computer Vision (2018)

    Google Scholar 

  57. Zhang, P., et al.: Learning spatial-temporal representations over walking tracklet for long-term person re-identification in the wild. IEEE Trans. Multimed. 23, 3562–3576 (2020)

    Article  Google Scholar 

  58. Zhang, Z., et al.: Semi-supervised face frontalization in the wild. IEEE Trans. Inf. Forensics Secur. 16, 909–922 (2021)

    Article  Google Scholar 

  59. Zheng, M., et al.: RPIfield: a new dataset for temporally evaluating person re-identification (2018)

    Google Scholar 

  60. Zhu, X., et al.: face alignment in full pose range: a 3D total solution. CoRR (2018)

    Google Scholar 

Download references

Acknowledgements

This work has been financially supported by national grant from the FCT (Fundação para a Ciência e a Tecnologia), under the project UIDB/05567/2020. Diego Haddad would like to thank the Brazillian organization National Council for Scientific and Technological Development (CNPq) for its financial support.

Author information

Authors and Affiliations

  1. Celso Suckow da Fonseca Federal Center of Technological Education, Rio de Janeiro, Brazil

    Anderson Manhães, Gabriel Matos, Milena F. Pinto, Jeferson Colares, Diego Brandão & Diego Haddad

  2. Smart Cities Research Center, Polytechnic Institute of Tomar, Tomar, Portugal

    Douglas O. Cardoso

  3. Research Centre in Digitalization and Intelligent Robotics, Polytechnic Institute of Bragança, Bragança, Portugal

    Diego Brandão

  4. Research Center in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-253, Bragança, Portugal

    Paulo Leitão

Authors
  1. Anderson Manhães
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gabriel Matos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Douglas O. Cardoso
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Milena F. Pinto
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jeferson Colares
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Paulo Leitão
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Diego Brandão
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Diego Haddad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Diego Haddad .

Editor information

Editors and Affiliations

  1. Instituto Politécnico de Bragança, Bragança, Portugal

    Ana I. Pereira

  2. University of Ljubljana, Ljubljana, Slovenia

    Andrej Košir

  3. Instituto Politécnico de Bragança, Bragança, Portugal

    Florbela P. Fernandes

  4. Instituto Politécnico de Bragança, Bragança, Portugal

    Maria F. Pacheco

  5. Instituto Politécnico de Bragança, Bragança, Portugal

    João P. Teixeira

  6. Instituto Politécnico de Bragança, Bragança, Portugal

    Rui P. Lopes

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Manhães, A. et al. (2022). Long-Term Person Reidentification: Challenges and Outlook. In: Pereira, A.I., Košir, A., Fernandes, F.P., Pacheco, M.F., Teixeira, J.P., Lopes, R.P. (eds) Optimization, Learning Algorithms and Applications. OL2A 2022. Communications in Computer and Information Science, vol 1754. Springer, Cham. https://doi.org/10.1007/978-3-031-23236-7_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-23236-7_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23235-0

  • Online ISBN: 978-3-031-23236-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation