CHROMAWEAR: An AI Assisted Personalized Fashion Design Recommender System for Diverse Skin Tones to Enhance Fashion Choices

Year : 2024 | Volume : 11 | Issue : 02 | Page : 49 58
    By

    Humaira Naaz,

  • Shabnam Bano,

  • Guddu Kumar,

  • Mohammad Faizan,

  • Sunil,

  1. Student, Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India
  2. Student, Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India
  3. Student, Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India
  4. Student, Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India
  5. Associate Professor, Department of Computer Engineering, Jamia Millia Islamia, New Delhi, India

Abstract

In the age of fashion, we receive the same question every day: What should I wear today to look nice? The fashion industry is always changing, welcoming inclusivity and diversity. Still, despite the advancements, there are difficulties meeting people’s varied needs, especially with regard to skin tone. This research work presents a personalized fashion design recommender system with artificial intelligence support to improve the fashion choices of people with different skin tones. Unlike traditional techniques of physically trying on garments, the skin tonner system uses digital representations to revolutionize the experience of putting on clothing. The system makes customized fashion recommendations by analyzing skin tone and taking into account color palettes, fabric textures, and clothing styles that fit well with the individual’s complexion. Through augmented reality and machine learning, the skin tonner system analyses skin tone and body shape to generate abstract digital overlays of clothing for users to interact with in real-time. With the use of numerous algorithms and models, such as the Decision Tree algorithm for recommendation and the K-Means Clustering algorithm for skin tone shade detection, the proposed system seeks to assist the user in selecting what color clothing will suit the individual best on appropriate occasions. We go over the design, implementation process, and assessment criteria of the suggested system, highlighting its potential to transform the fashion business by advancing inclusivity and giving people the power to make educated fashion choices.

Keywords: Artificial intelligence (AI), fashion design, recommender system, skin tone, diversity, inclusivity

[This article belongs to Journal of Artificial Intelligence Research & Advances ]

How to cite this article:
Humaira Naaz, Shabnam Bano, Guddu Kumar, Mohammad Faizan, Sunil. CHROMAWEAR: An AI Assisted Personalized Fashion Design Recommender System for Diverse Skin Tones to Enhance Fashion Choices. Journal of Artificial Intelligence Research & Advances. 2024; 11(02):49-58.
How to cite this URL:
Humaira Naaz, Shabnam Bano, Guddu Kumar, Mohammad Faizan, Sunil. CHROMAWEAR: An AI Assisted Personalized Fashion Design Recommender System for Diverse Skin Tones to Enhance Fashion Choices. Journal of Artificial Intelligence Research & Advances. 2024; 11(02):49-58. Available from: https://journals.stmjournals.com/joaira/article=2024/view=155863


References

  1. Narode PP, Bagul S., Pardeshi K, Paymode V, Madhawai P. Improving Foundation Shade Recommendations using Skin Tone Recognition. Int J Adv Res Sci Commun Technol. 2023; 3(2): 668–676. 10.48175/IJARSCT-14290.
  2. Ziccardi Giovanni. Wearable Technologies and Smart Clothes in the Fashion Business: Some Issues Concerning Cybersecurity and Data Protection. Laws. 2020. 9(2): 12. 10.3390/laws9020012.
  3. Kolkur, Kalbande D, Jatakia J. Human skin detection using rgb, hsv and ycbcr color models. Proceedings of the International Conference on Communication and Signal Processing 2016 (ICCASP 2016). 2016 Feb; 324–332.
  4. Garude Digant, Khopkar Anushree, Dhake Monali, Laghane Shivani, Maktum Tabassum. Skin-Tone and Occasion Oriented Outfit Recommendation System. SSRN Electronic Journal. 2019. 10.2139/ssrn.3368058.
  5. Al-Tairi ZH, Rahmat RW, Saripan MI, Sulaiman PS. Skin segmentation using YUV and RGB color spaces. J Inf Process Syst. 2014;10(2):283–99. DOI: 10.3745/JIPS.02.0002.
  6. Patil Shankar, Bhanage Simran, Shali Zitin. Automatic Suggestion of Outfits using Image Processing. Int Res J Eng Technol. 2019; 6(4): 4129–4135.
  7. Atharv P. A Review on Clothes Matching and Recommendation Systems based on user Attributes. Int J Eng Res Technol. 2020; 9(8): 786–791. V9. 10.17577/IJERTV9IS080371.
  8. Yuan Miaolong, Khan Ishtiaq, Farbiz Farzam, Yao Ss. A Mixed Reality Virtual Clothes Try-on System. IEEE Trans Multimed. 2013 Dec; 15(8): 1958–1968. 10.1109/TMM.2013.2280560.
  9. Siddhesh T, Chetan P, Yaman T, Sumeet KS, Monika D. Suggestion Based Outfit Selection Using Skin Tone Detection in Augmented Reality. Int J Res Appl Sci Eng Technol (IJRASET). 2016; 4(V): 870–872. ISSN: 2321-9653.
  10. Bhure Bhagyshree, Bansod Pratiksha, Amgaokar Monali, Lodiwale Savita, Orkey Anjali, Mohod Ashish. A Review on Outfit Fashion Recommendation System. Int J Sci Res Comput Sci Eng Inf Technol. 2021; 7(3): 220–222. 10.32628/CSEIT217368.
  11. Ying Huang, Tao Huang. Outfit Recommendation System Based on Deep Learning. 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA), Advances in Computer Science Research. 2017; 164–168.
  12. Liu Yu, Nie Jingwen, Xu Lexi, Chen Yue, Xu Bingyu. Clothing Recommendation System Based on Advanced User-Based Collaborative Filtering Algorithm. Signal and Information Processing, Networking and Computers (ICSINC 2017). Singapore: Springer; 2018; 436–443. 10.1007/978-981-10-7521-6_53.
  13. Syed MA, Yash S, Pragati V, Siniya V. Study of the role that AI can play in the Sustainable Fashion Business. International Journal of Scientific Research in Engineering and Management (IJSREM). 2024;8(2):1–7. 10.10.55041/IJSREM28523.
  14. Shirkhani Shaghayegh, Mokayed Hamam, Saini Rajkumar, Hum Yan. Study of AI-Driven Fashion Recommender Systems. SN Comput Sci. 2023; 4(5): 514. 10.1007/s42979-023-01932-9.
  15. Saturday Nne, Igulu Kingsley, Singh Thipendra P, Onuodu F. The Role of Machine Learning/AI in Recommender Systems. CRC Press Florida, United States; 2023. 10.1201/9781003319122-5.
  16. Wang Linyi. Commerce Product Recommendation Algorithm Based on Collaborative Filtering. In: Intelligent Computing Technology and Automation. IOS Press, Amsterdam. 2024; 617–624. 10.3233/ATDE231237
Regular Issue Subscription Review Article
Volume 11
Issue 02
Received 24/05/2024
Accepted 11/06/2024
Published 10/07/2024
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