FINAL PPT ONLINE COSMETIC SHOPPING.pptx application

FINAL PPT ONLINE COSMETIC SHOPPING.pptx

1. ONLINE COSMETIC SHOPPING WITH SKINTONE DETECTION USING ARTIFICIAL INTELLIGENCE TEAM MEMBERS GUIDED BY Kiruthiga S - 962819205013 Dr. J. Vijila Manju P - 962819205018 Assistant Professor Priyadharshini D - 962819205024 Department of IT

2. OVERVIEW Objective Scope Existing system Limitations of existing system Proposed system Introduction Literature survey System Architecture Modules and Description Result and Discussion Conclusion References

3. OBJECTIVE To identify and detect the individual’s skin tone based on images and recommend the products that matches the user’s skin tone.

4. SCOPE It allows the users to shop the cosmetic product from the comfort of their own homes, instead of physically going to a store . It allows the users to identify the suitable products based on the skin tone. It can provide personalized recommendations based on a customer’s purchase history , preferences and browsing behavior.

5. EXISTING SYSTEM In the online cosmetic shopping typically involves users browsing a website and selecting products they are interested in and adding them to their cart for purchase. Users must manually determine their own skin tone and select makeup shades based on their own judgment.

6. LIMITATIONS OF EXISTING SYSTEM A higher chance of ordering a product , that doesn’t match one’s skin tone or complexation . User don’t have an idea about choosing the suitable items. It may not be able to provide personalized recommendations based on a person's specific skin type.

7. PROPOSED SYSTEM The system would incorporate an AI algorithm capable of detecting a customer's skin tone accurately. It analyzes a photo of the customer's face to determine their skin tone. The system would use the skin tone detection algorithm and customer profile information to generate personalized product recommendations. Customers would be presented with cosmetic products that are likely to match their skin tone and meet their specific needs and preferences

8. INTRODUCTION Online shopping has become increasingly popular, and cosmetic products are no exception. However, the lack of physical interaction with products and the inability to test them on oneself has made it challenging for users to choose products that best match their skintone . This project proposes an innovative approach to address this issue by incorporating skin tone detection technology into online cosmetic shopping. The platform uses the latest advancements in AI(K-Means Clustering) Algorithm to detect your unique skin tone and recommend products that will complement and enhance your natural beauty.

9. LITERATURE SURVEY AUTHOR TITLE JOURNAL METHODS DEMERITS A Kanimozhi , K Mukesh Kumar and A Balaji Human Skin Tone Detection International Journal of Engineering Research in Electronics and Communication Engineering (IJERECE) - 2022 Author uses elliptical boundary model with better performance. It might not accurately represent the complex and diverse variations in human skin tones . J. H. Kim and H. J. Kim Personalized Cosmetics Recommendation Based on Skin Color and Skin Type Using DL Tamkang Journal of Science and Engineering - 2020 Gaussian distribution. Fixed parameters, as mean and variance, which determine the shape and characteristics.

10. AUTHOR TITLE JOURNAL METHODS DEMERITS Ahmed Elgammal , Crystal Muang , Dunxu Hu Skin Detection - a Short Tutorial Department of Computer Science, Rutgers University, Piscataway, USA- 2019 Bayes rule method Accurate estimation of prior probabilities can be challenging, particularly if the training data is imbalanced S. Kolkur , D. Kalbande , P. Shimpi , C. Bapat , J. Jatakia Human Skin Detection Using RGB, HSV and YCbCr Color Models Bulletin of Electrical Engineering and Informatics-2017 Threshold based methodology skin tones may fall outside the threshold range, leading to inaccurate results or biased detection.

11. SYSTEM ARCHITECTURE

12. LEVEL 0 DFD

13. LEVEL 1 DFD

14. LEVEL 2 DFD

15. MODULES MODULE1: PRODUCT ORGANISATION MODULE Product catalog management Allow the administrator to add , update and delete products including information such as product including information. Customer management Manage customer accounts, Manage customer orders and returns, Update customer information. Order management Manage orders, Processing orders, Shipping orders

16. MODULE 2 : SKINTONE DETECTOR Data collection Collect large dataset of images containing human faces represent various skin tone Skin tone detection Use CNN to detect the user’s skin tone from the uploaded image. Cosmetic recommendation Based on the user’s skin tone recommend cosmetic products that would be most suitable for them. Testing and evaluation The trained model can be tested on a separate set of images to evaluate its performance .

17. MODULE 3 : CHATBOT CREATION The chat bot module in a cosmetic shopping system with skin tone detection is an interactive component that provides personalized assistance and guidance to users during their shopping journey. It combines the functionality of a chat bot with the skin tone detection module to offer a comprehensive and tailored experience.

18. RESULT AND DISCUSSION ADMIN LOGIN

19. ADMIN DASHBOARD

20. CREATE PRODUCT LIST

21. BRAND LIST

22. ORDER LIST

23. USER HOME PAGE

24. LOGIN / REGISTER PAGE FOR USERS

25. CART LIST PAGE

26. ORDERING A PRODUCT

27. PAYMENT FORM

28. SKINTONE DETECTOR HOMEPAGE

29. PAGE FOR UPLOAD THE IMAGE

30. UPLOADED IMAGE

31. SKIN TONE DETECTION PAGE

32. CHATBOT

33. CONCLUSION The proposed solution for the online cosmetic shopping with skin tone detection has useful features that will make it easier for customers to buy the cosmetic products that suits for their skin tone. The integration of skin tone detection technology into online cosmetic shopping platform can help overcome lot of challenges. The system will provide chatbot to clarify the issues faced by the customers while shopping. Overall, the proposed solution for the online cosmetic shopping with skin tone detection providing a more personalized shopping experience, increased customer satisfaction, loyalty, and sales.

34. REFERENCES [1 ] A Kanimozhi , K Mukesh Kumar, A Balaji , (2022), “ Human Skin Tone Detection”, International Journal of Engineering Research in Electronics and Communication Engineering (IJERECE) , Vol. 9, no. 2, pp : 2394-6849 . [2] J. H. Kim and H. J. Kim,(2020), "Personalized Cosmetics Recommendation Based on Skin Color and Skin Type Using Deep Learning", Tamkang Journal of Science and Engineering, Vol. 6, no. 5, pp : 227–234. [3] S. J. Kim and J. Kim, (2019), "Online cosmetics shopping behavior: The effects of traits and product characteristics", Proc. Seventh International Symposium on Signal Processing and Its Applications, Vol. 1, no. 6, pp. 525-528. [4] Ahmed Elgammal , Crystal Muang , Dunxu Hu, (2019), “Skin Detection - a Short Tutorial”, Department of Computer Science, Rutgers University, Piscataway, USA, Vol. 3656, no. 1999, pp : 458-466

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