Skip to main content
SpringerLink
Log in

Person Quick-Search Approach Based on a Facial Semantic Attributes Description

  • Conference paper
  • First Online:
Advanced Concepts for Intelligent Vision Systems (ACIVS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14124))

  • 193 Accesses

Abstract

Person search based on semantic attributes description presents an interest task for intelligent video surveillance applications. The main objective is to locate a suspect or to find a missing person in public areas using a semantic description (e.g. a 40-year-old asian woman) provided by an eyewitness. Such a description provides the facial soft biometric related to the facial semantic attributes (i.e. age, gender and ethnicity). In this paper, we introduced a new approach for person search named “Quick-Search” based on a facial semantic attributes description to enhance the person search task in an unconstrained environment. The main contribution of the paper is to introduce a multi-attributes score fusion method which relies on soft biometric features (age, gender, ethnicity) to improve the person search in a large dataset. An experimental study was conducted on the challenging FairFace dataset to validate the effectiveness of the proposed person search approach.

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 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 74.99
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. Agbo-Ajala, O., Viriri, S.: Deep learning approach for facial age classification: a survey of the state-of-the-art. Artif. Intell. Rev. 54, 1–35 (2020)

    Google Scholar 

  2. Ahmed, M.A., Choudhury, R.D., Kashyap, K.: Race estimation with deep networks. J. King Saud Univ. Comput. Inf. Sci. 34, 4579–4591 (2020)

    Google Scholar 

  3. Aslam, A., Hussain, B., Cetin, A.E., Umar, A.I., Ansari, R.: Gender classification based on isolated facial features and foggy faces using jointly trained deep convolutional neural network. J. Electron. Imaging 27(5), 053–023 (2018)

    Article  Google Scholar 

  4. Chen, L., Fan, C., Yang, H., Hu, S., Zou, L., Deng, D.: Face age classification based on a deep hybrid model. SIViP 12(8), 1531–1539 (2018)

    Article  Google Scholar 

  5. Dammak, S., Mliki, H., Fendri, E.: Gender effect on age classification in an unconstrained environment. Multimedia Tools Appl. 80(18), 28001–28014 (2021)

    Article  Google Scholar 

  6. Dammak, S., Mliki, H., Fendri, E.: Gender estimation based on deep learned and handcrafted features in an uncontrolled environment. Multimedia Syst. 29, 1–13 (2022)

    Google Scholar 

  7. Duan, M., Li, K., Yang, C., Li, K.: A hybrid deep learning CNN-ELM for age and gender classification. Neurocomputing 275, 448–461 (2018)

    Article  Google Scholar 

  8. Eidinger, E., Enbar, R., Hassner, T.: Age and gender estimation of unfiltered faces. IEEE Trans. Inf. Forensics Secur. 9(12), 2170–2179 (2014)

    Article  Google Scholar 

  9. Frikha, M., Fendri, E., Hammami, M.: People search based on attributes description provided by an eyewitness for video surveillance applications. Multimedia Tools Appl. 78, 2045–2072 (2019)

    Article  Google Scholar 

  10. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  11. Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)

    Article  Google Scholar 

  12. Jagtap, J., Kokare, M.: Human age classification using facial skin aging features and artificial neural network. Cogn. Syst. Res. 40, 116–128 (2016)

    Article  Google Scholar 

  13. Kärkkäinen, K., Joo, J.: Fairface: Face attribute dataset for balanced race, gender, and age. arXiv preprint arXiv:1908.04913 pp. 1–11 (2019)

  14. Kittler, J., Hatef, M., Duin, R.P., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)

    Article  Google Scholar 

  15. Li, S., Xiao, T., Li, H., Zhou, B., Yue, D., Wang, X.: Person search with natural language description. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1970–1979 (2017)

    Google Scholar 

  16. Luong, T.K., Hsiung, P.A., Han, Y.T.: Improve gender, race, and age classification with supervised contrastive learning (2021). https://doi.org/10.13140/RG.2.2.14680.01286

  17. Mohamed, S., Nour, N., Viriri, S.: Gender identification from facial images using global features. In: Conference on Information Communications Technology and Society (ICTAS), pp. 1–6. IEEE (2018)

    Google Scholar 

  18. Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)

    Article  Google Scholar 

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

    Google Scholar 

  20. Serna, I., Pena, A., Morales, A., Fierrez, J.: InsideBias: measuring bias in deep networks and application to face gender biometrics. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 3720–3727. IEEE (2021)

    Google Scholar 

  21. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: arXiv preprint arXiv:1409.1556, pp. 1–14 (2014)

  22. Smulyan, H., Asmar, R.G., Rudnicki, A., London, G.M., Safar, M.E.: Comparative effects of aging in men and women on the properties of the arterial tree. J. Am. Coll. Cardiol. 37(5), 1374–1380 (2001)

    Article  Google Scholar 

  23. Sveikata, K., Balciuniene, I., Tutkuviene, J.: Factors influencing face aging. Lit. Revi. Stomatologija 13(4), 113–116 (2011)

    Google Scholar 

  24. Wang, J., Feng, S., Cheng, Y., Al-Nabhan, N.: Survey on the loss function of deep learning in face recognition. J. Inf. Hiding Priv. Prot. 3(1), 29–47 (2021)

    Google Scholar 

  25. 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 

  26. Zhang, L., Chu, R., Xiang, S., Liao, S., Li, S.Z.: Face detection based on multi-block LBP representation. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 11–18. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74549-5_2

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. MIRACL-FS, Faculty of Sciences of Sfax, University of Sfax, Sfax, Tunisia

    Sahar Dammak & Emna Fendri

  2. MIRACL Laboratory, University of Sfax, Sfax, Tunisia

    Hazar Mliki

  3. National Institute of Applied Science and Technology, University of Carthage, Tunis, Tunisia

    Hazar Mliki

Authors
  1. Sahar Dammak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hazar Mliki
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Emna Fendri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hazar Mliki .

Editor information

Editors and Affiliations

  1. DGA TA, Toulouse, France

    Jaques Blanc-Talon

  2. University of Auckland, Auckland, New Zealand

    Patrice Delmas

  3. Ghent University, Ghent, Belgium

    Wilfried Philips

  4. University of Antwerp, Wilrijk, Belgium

    Paul Scheunders

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

Dammak, S., Mliki, H., Fendri, E. (2023). Person Quick-Search Approach Based on a Facial Semantic Attributes Description. In: Blanc-Talon, J., Delmas, P., Philips, W., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2023. Lecture Notes in Computer Science, vol 14124. Springer, Cham. https://doi.org/10.1007/978-3-031-45382-3_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-45382-3_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-45381-6

  • Online ISBN: 978-3-031-45382-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation