Skip to main content
Springer Nature Link
Log in

Consensus Ranking for Efficient Face Image Retrieval: A Novel Method for Maximising Precision and Recall

  • Conference paper
  • First Online:
Image Analysis and Processing – ICIAP 2023 (ICIAP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14233))

Included in the following conference series:

  • 822 Accesses

Abstract

Efficient face image retrieval, i.e. searching for existing photographs of a person in unlabelled photo collections using a query photo, is evaluated for a novel method to find the top n results for Consensus Ranking. The approach aims to maximise precision and recall by using the retrieved photos, all ranked on similarity. The proposed method aims to retrieve all photos of the queried person while excluding images of other individuals. To achieve this, the method uses the top n results as temporary queries, recalculates similarities, and combines the obtained ranked lists to produce a better overall ranking. The method includes a novel and reliable procedure for selecting n, which is evaluated on two datasets, and considers the impact of age variation in the datasets.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Beitzel, S.M., Jensen, E.C., Frieder, O.: Map. In: Liu, L., Özsu, M.T. (eds.) Encyclopedia of Database Systems, pp. 1691–1692. Springer, Boston (2009). https://doi.org/10.1007/978-0-387-39940-9_492

  2. Bhattacharyya, A.: On a measure of divergence between two statistical populations defined by their probability distribution. Bull. Calcutta Math. Soc. 35, 99–110 (1943)

    MathSciNet  MATH  Google Scholar 

  3. Chen, T., et al.: Mxnet: a flexible and efficient machine learning library for heterogeneous distributed systems. CoRR abs/1512.01274 (2015). http://arxiv.org/abs/1512.01274

  4. Choy, C.B., Gwak, J.Y., Savarese, S., Chandraker, M.: Universal correspondence network. In: Proceedings of the 30th International Conference on Neural Information Processing Systems, NIPS 2016, pp. 2414–2422. Curran Associates Inc., Red Hook (2016)

    Google Scholar 

  5. Deng, J., Guo, J., Xue, N., Zafeiriou, S.: Arcface: additive angular margin loss for deep face recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4690–4699 (2019)

    Google Scholar 

  6. Duchenne, O., Bach, F., Kweon, I.S., Ponce, J.: A tensor-based algorithm for high-order graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2383–2395 (2011). https://doi.org/10.1109/TPAMI.2011.110

    Article  Google Scholar 

  7. Gupta, A., Pawade, P., Balakrishnan, R.: Deep residual network and transfer learning-based person re-identification. Intell. Syst. Appl. 16, 200137 (2022). https://doi.org/10.1016/j.iswa.2022.200137. https://www.sciencedirect.com/science/article/pii/S2667305322000746

  8. Hast, A.: Simple filter design for first and second order derivatives by a double filtering approach. Pattern Recogn. Lett. 42, 65–71 (2014). https://doi.org/10.1016/j.patrec.2014.01.014. https://www.sciencedirect.com/science/article/pii/S0167865514000282

  9. Hast, A.: Consensus ranking for increasing mean average precision in keyword spotting. In: Amelio, A., Borgefors, G., Hast, A. (eds.) Proceedings of 2nd International Workshop on Visual Pattern Extraction and Recognition for Cultural Heritage Understanding co-located with 16th Italian Research Conference on Digital Libraries (IRCDL 2020), Bari, Italy, 29 January 2020, CEUR Workshop Proceedings, vol. 2602, pp. 46–57. CEUR-WS.org (2020). https://ceur-ws.org/Vol-2602/paper4.pdf

  10. InsightFace: Insightface (2023). https://insightface.ai. Accessed 30 Feb 2023

  11. Jang, Y.K., Jeong, D., Lee, S.H., Cho, N.I.: Deep clustering and block hashing network for face image retrieval. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11366, pp. 325–339. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20876-9_21

    Chapter  Google Scholar 

  12. Lin, J., Li, Z., Tang, J.: Discriminative deep hashing for scalable face image retrieval. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-2017, pp. 2266–2272 (2017). https://doi.org/10.24963/ijcai.2017/315

  13. Liu, W., Wen, Y., Yu, Z., Li, M., Raj, B., Song, L.: Sphereface: deep hypersphere embedding for face recognition. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6738–6746. IEEE Computer Society, Los Alamitos (2017). https://doi.org/10.1109/CVPR.2017.713

  14. Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., Zafeiriou, S.: Agedb: the first manually collected, in-the-wild age database. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1997–2005 (2017). https://doi.org/10.1109/CVPRW.2017.250

  15. Opitz, J., Burst, S.: Macro f1 and macro f1 (2021)

    Google Scholar 

  16. Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. In: Xie, X., Jones, M.W., Tam, G.K.L. (eds.) Proceedings of the British Machine Vision Conference (BMVC), pp. 41.1–41.12. BMVA Press (2015). https://doi.org/10.5244/C.29.41

  17. Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. Curran Associates Inc., Red Hook (2019)

    Google Scholar 

  18. Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 815–823 (2015). https://doi.org/10.1109/CVPR.2015.7298682

  19. Serengil, S.I., Ozpinar, A.: Lightface: a hybrid deep face recognition framework. In: 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), pp. 23–27. IEEE (2020). https://doi.org/10.1109/ASYU50717.2020.9259802

  20. Serengil, S.I., Ozpinar, A.: Hyperextended lightface: a facial attribute analysis framework. In: 2021 International Conference on Engineering and Emerging Technologies (ICEET), pp. 1–4. IEEE (2021). https://doi.org/10.1109/ICEET53442.2021.9659697

  21. Serengil, S.I., Ozpinar, A.: An evaluation of sql and nosql databases for facial recognition pipelines (2023). https://www.cambridge.org/engage/coe/article-details/63f3e5541d2d184063d4f569. https://doi.org/10.33774/coe-2023-18rcn, preprint

  22. Shi, Y., Jain, A.K.: Boosting unconstrained face recognition with auxiliary unlabeled data. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2789–2798. IEEE Computer Society, Los Alamitos (2021). https://doi.org/10.1109/CVPRW53098.2021.00314

  23. Shi, Y., Jain, A.: Probabilistic face embeddings. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 6901–6910 (2019). https://doi.org/10.1109/ICCV.2019.00700

  24. Tang, J., Li, Z., Zhu, X.: Supervised deep hashing for scalable face image retrieval. Pattern Recogn. 75(C), 25–32 (2018). https://doi.org/10.1016/j.patcog.2017.03.028

  25. Tang, J., Lin, J., Li, Z., Yang, J.: Discriminative deep quantization hashing for face image retrieval. IEEE Trans. Neural Netw. Learn. Syst. 29(12), 6154–6162 (2018). https://doi.org/10.1109/TNNLS.2018.2816743

    Article  Google Scholar 

  26. Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991). https://doi.org/10.1162/jocn.1991.3.1.71

    Article  Google Scholar 

  27. Viola, P., Jones, M.: Robust real-time face detection. In: Proceedings Eighth IEEE International Conference on Computer Vision, ICCV 2001, vol. 2, pp. 747–747 (2001). https://doi.org/10.1109/ICCV.2001.937709

  28. Wang, H., et al.: Cosface: large margin cosine loss for deep face recognition. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5265–5274 (2018). https://doi.org/10.1109/CVPR.2018.00552

  29. Wen, Y., Zhang, K., Li, Z., Qiao, Yu.: A discriminative feature learning approach for deep face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 499–515. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_31

    Chapter  Google Scholar 

  30. Yang, M.H., Kriegman, D., Ahuja, N.: Detecting faces in images: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 24(1), 34–58 (2002). https://doi.org/10.1109/34.982883

    Article  Google Scholar 

  31. Yi, D., Lei, Z., Liao, S., Li, S.Z.: Learning face representation from scratch (2014). https://doi.org/10.48550/ARXIV.1411.7923. https://arxiv.org/abs/1411.7923

  32. Zaeemzadeh, A., et al.: Face image retrieval with attribute manipulation. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 12096–12105 (2021). https://doi.org/10.1109/ICCV48922.2021.01190

  33. Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 23(10), 1499–1503 (2016). https://doi.org/10.1109/LSP.2016.2603342

    Article  Google Scholar 

  34. Zhang, Z., Luo, P., Loy, C.C., Tang, X.: Learning deep representation for face alignment with auxiliary attributes. IEEE Trans. Pattern Anal. Mach. Intell. 38(5), 918–930 (2016). https://doi.org/10.1109/TPAMI.2015.2469286

    Article  Google Scholar 

Download references

Acknowledgments

This work has been partially supported by the Swedish Research Council (Dnr 2020-04652; Dnr 2022-02056) in the projects The City’s Faces. Visual culture and social structure in Stockholm 1880–1930 and The International Centre for Evidence-Based Criminal Law (EB-CRIME).

Author information

Authors and Affiliations

  1. Uppsala University, 751 05, Uppsala, Sweden

    Anders Hast

Authors
  1. Anders Hast
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anders Hast .

Editor information

Editors and Affiliations

  1. University of Udine, Udine, Italy

    Gian Luca Foresti

  2. University of Udine, Udine, Italy

    Andrea Fusiello

  3. University of York, York, UK

    Edwin Hancock

Rights and permissions

Reprints and permissions

Copyright information

© 2023 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

Hast, A. (2023). Consensus Ranking for Efficient Face Image Retrieval: A Novel Method for Maximising Precision and Recall. In: Foresti, G.L., Fusiello, A., Hancock, E. (eds) Image Analysis and Processing – ICIAP 2023. ICIAP 2023. Lecture Notes in Computer Science, vol 14233. Springer, Cham. https://doi.org/10.1007/978-3-031-43148-7_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-43148-7_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43147-0

  • Online ISBN: 978-3-031-43148-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics