Skip to main content
Springer Nature Link
Log in

A Neural Network-Based Approach to Identifying Wrinkles and Recommending Cosmetic Products

  • Conference paper
  • First Online:
Optimization, Learning Algorithms and Applications (OL2A 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2280))

Abstract

Skincare has become a constant demand among the population, who are increasingly concerned about their health. Furthermore, environmental issues arouse the interest of the masses in natural and sustainable products. This project proposes an approach for recommending thermal-based products based on a set of information provided by the user, combined with the results of computer vision algorithms (to identify the age and occurrence of wrinkles on the user’s forehead). A list of recommended products is generated Based on the profile determined for the user. To predict wrinkles, for each facial image sent by the user, we apply a pre-processing step that segments and prepares the region of interest, which a CNN will process. As a CNN, we used the VGG16 architecture trained using a transfer learning and fine-tuning strategy, which improved the results obtained, reaching an accuracy of 92% in classifying wrinkles. An algorithm provided by the Deepface tool is used to predict the user’s age, based on the sent picture. Which is crossed with the user’s information to determine a level of aging in order to improve the quality of the recommended products.

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. Alrabiah, A., Alduailij, M., Crane, M.: Computer-based approach to detect wrinkles and suggest facial fillers. Int. J. Adv. Comput. Sci. Appl. 10(9) (2019)

    Google Scholar 

  2. Aquavalor: Aquae vitae – Água termal como fonte de vida e saúde (2022). https://aquavalor.pt/2022/03/29/aquae-vitae-agua-termal-como-fonte-de-vida-e-saude/

  3. Batool, N., Chellappa, R.: Fast detection of facial wrinkles based on gabor features using image morphology and geometric constraints. Pattern Recogn. 48(3), 642–658 (2015)

    Article  Google Scholar 

  4. Caliva, F., Iriondo, C., Martinez, A.M., Majumdar, S., Pedoia, V.: Distance map loss penalty term for semantic segmentation. arXiv preprint arXiv:1908.03679 (2019)

  5. Cetinic, E., Lipic, T., Grgic, S.: Fine-tuning convolutional neural networks for fine art classification. Expert Syst. Appl. 114, 107–118 (2018)

    Article  Google Scholar 

  6. Chen, H.F., Lee, Y.H., Tu, Y.C., Chao, Y.F.: Consumer purchase intention for skincare products (2012)

    Google Scholar 

  7. Chin, J., Jiang, B.C., Mufidah, I., Persada, S.F., Noer, B.A.: The investigation of consumers’ behavior intention in using green skincare products: a pro-environmental behavior model approach. Sustainability 10(11), 3922 (2018)

    Article  Google Scholar 

  8. Deeptag: Deeptag AI. https://deeptag.ai/DeepTagPage.html

  9. Dermalogica: Dermalogica face mapping. https://www.dermalogica.co.uk/pages/face-mapping

  10. Garnier: Skin coach AI. https://www.garnier.com.au/skin-coach-skin-analysis

  11. Groupe, L.: Skinconsult AI vichy. https://www.loreal.com/en/articles/science-and-technology/skinconsult-ai-vichy/

  12. Gu, J., et al.: Recent advances in convolutional neural networks. Pattern Recogn. 77, 354–377 (2018)

    Article  Google Scholar 

  13. Haut.AI: Hautai – AI skin analysis. https://haut.ai

  14. Ignae: Ai skin analysis. https://ignae.com/pages/ai-skin-analysis

  15. Kim, S., Yoon, H., Lee, J., Yoo, S.: Semi-automatic labeling and training strategy for deep learning-based facial wrinkle detection. In: IEEE International Symposium on Computer-based Medical Systems (CBMS), pp. 383–388. IEEE (2022)

    Google Scholar 

  16. Kokoi, I.: Female buying behaviour related to facial skin care products (2011)

    Google Scholar 

  17. Lee, Y.B., et al.: Perceptions and behavior regarding skin health and skin care products: analysis of the questionnaires for the visitors of skin health expo 2018. Ann. Dermatol. 32(5), 375 (2020)

    Article  Google Scholar 

  18. Liu, Z., Qi, Q., Wang, S., Zhai, G.: A novel approach to the detection of facial wrinkles: database, detection algorithm, and evaluation metrics. Comput. Biol. Med. 108431 (2024)

    Google Scholar 

  19. Lugaresi, C., et al.: Mediapipe: a framework for perceiving and processing reality. In: Workshop on Computer Vision for AR/VR at IEEE Computer Vision and Pattern Recognition (CVPR), vol. 2019 (2019)

    Google Scholar 

  20. Lululab: Lumini software development kit. https://www.lulu-lab.com/

  21. Ng, C.C., Yap, M.H., Costen, N., Li, B.: Automatic wrinkle detection using hybrid hessian filter. In: Asian Conference on Computer Vision, pp. 609–622. Springer (2015)

    Google Scholar 

  22. Ng, C.C., Yap, M.H., Costen, N., Li, B.: Wrinkle detection using hessian line tracking. IEEE Access 3, 1079–1088 (2015)

    Article  Google Scholar 

  23. Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The feret evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1090–1104 (2000)

    Article  Google Scholar 

  24. Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.J.: The feret database and evaluation procedure for face-recognition algorithms. Image Vis. Comput. 16(5), 295–306 (1998)

    Article  Google Scholar 

  25. Poojary, R., Pai, A.: Comparative study of model optimization techniques in fine-tuned CNN models. In: International Conference on Electrical and Computing Technologies and Applications (ICECTA), pp. 1–4 (2019). https://doi.org/10.1109/ICECTA48151.2019.8959681

  26. Revieve: Ai skin diagnostics. https://www.revieve.com/platform/skincare/ai-skin-diagnostics

  27. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and computer-Assisted Intervention (MICCAI), pp. 234–241. Springer (2015)

    Google Scholar 

  28. Serengil, S.I., Ozpinar, A.: Hyperextended lightface: a facial attribute analysis framework. In: International Conference on Engineering and Emerging Technologies (ICEET), pp. 1–4. IEEE (2021). https://doi.org/10.1109/ICEET53442.2021.9659697, https://ieeexplore.ieee.org/document/9659697

  29. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  30. SkinQ: Skinq AI mirror. https://skinq.com/pages/ai-mirror

  31. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)

    Google Scholar 

  32. TensorFlow: an end-to-end platform for machine learning. https://www.tensorflow.org/

  33. Vrbančič, G., Podgorelec, V.: Transfer learning with adaptive fine-tuning. IEEE Access 8, 196197–196211 (2020)

    Article  Google Scholar 

  34. Yusuf, N., Irby, C., Katiyar, S.K., Elmets, C.A.: Photoprotective effects of green tea polyphenols. Photodermatology, photoimmunology photomedicine 23(1), 48–56 (2007)

    Article  Google Scholar 

  35. 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)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by “Fundação La Caixa” and by “Fundação para a Ciência e a Tecnologia” under the scope of the project “Aquae Vitae - Água Termal como Fonte de Vida e Saúde” through the Promove Program (“Projetos I&D Mobilizadores”) and also this work was supported by national funds through FCT/MCTES (PIDDAC): CeDRI, UIDB/05757/2020 (DOI: 10.54499/UIDB/05757/2020) and UIDP/05757/2020 (DOI: 10.54499/UIDP/05757/2020); SusTEC, LA/P/0007/2020 (DOI: 10.54499/LA/P/0007/2020).

Portions of the research in this paper use the FERET database of facial images collected under the FERET program, sponsored by the DOD Counterdrug Technology Development Program Office [23, 24].

Author information

Authors and Affiliations

  1. Research Centre in Digitalization and Intelligent Robotics (CeDRI), Laborátorio para a Sutentabilidade e Tecnológia em Regiões de Montanha (SusTEC), Instituto Politécnico de Bragança, Campus de Santa Apolónia, Bragança, Portugal

    Guilherme de M. Tonello, Maria João Varanda Pereira & Paulo Alves

  2. Coordenação de Engenharia de Softaware, Universidade Tecnológica Federal do Paraná, Campus Dois Vizinhos, Paraná, Brazil

    Guilherme de M. Tonello & André Roberto Ortoncelli

Authors
  1. Guilherme de M. Tonello
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maria João Varanda Pereira
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paulo Alves
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. André Roberto Ortoncelli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guilherme de M. Tonello .

Editor information

Editors and Affiliations

  1. Instituto Politécnico de Bragança, Bragança, Portugal

    Ana I. Pereira

  2. Instituto Politécnico de Bragança, Bragança, Portugal

    Florbela P. Fernandes

  3. Escola Superior de Tecnologia e Gestão, Instituto Politécnico de Bragança, Bragança, Portugal

    João P. Coelho

  4. Instituto Politécnico de Bragança, Bragança, Portugal

    João P. Teixeira

  5. Department of Electrical Engineering, Instituto Politécnico de Bragança, Bragança, Portugal

    José Lima

  6. Instituto Politécnico de Bragança, Bragança, Portugal

    Maria F. Pacheco

  7. Instituto Politécnico de Bragança, Bragança, Portugal

    Rui P. Lopes

  8. Dept ISAATC, Universidad de La Laguna, San Cristóbal de La Laguna, Spain

    Santiago T. Álvarez

Ethics declarations

Disclosure of Interests

Author Guilherme Tonello has received research grants from “Fundação La Caixa” and by “Fundação para a Ciência e a Tecnologia” under the scope of the project “Aquae Vitae – Água Termal como Fonte de Vida e Saúde”.

Rights and permissions

Reprints and permissions

Copyright information

© 2024 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

de M. Tonello, G., Pereira, M.J.V., Alves, P., Roberto Ortoncelli, A. (2024). A Neural Network-Based Approach to Identifying Wrinkles and Recommending Cosmetic Products. In: Pereira, A.I., et al. Optimization, Learning Algorithms and Applications. OL2A 2024. Communications in Computer and Information Science, vol 2280. Springer, Cham. https://doi.org/10.1007/978-3-031-77426-3_12

Download citation

Publish with us

Policies and ethics