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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM33511

COSMETIC SUGGESTION BASED ON SKIN CONDITION

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM33511

Volume:

8 |

5 |

May 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM33511

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

SHC

Suchethana H C

HD

Himashree D

KBSR

Keerthana Bhatt S R

RGB

Ranjitha G B

SS

Smruthi S

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Abstract

These days, beauty and cosmetology are exceptionally basic to a person’s look. People get to a collection of things through online shopping and e-commerce websites. It is very challenging to choose the right cosmetic for human skin. Given that each person contains a specific type of skin, choosing right cosmetics that suit the person may be annoying. The composition depends on skin type, which can be dry, oily, or normal. Due to the significant crash that makeup have on a person’s looks, the client must to choose the finest cosmetics for themselves based on person components . Identification of product that does not include any harms or allergies for the clients is very important. Thus the point of this application is to highlight the current applications of AI and machine learning in beautification and cosmetology by thorough examination of skin condition and prescribe personalized cosmetics based on the type of skin and buy secure things for the skin utilizing the Convolutional Neural Network (CNN). The objective is to achieve tall precision and recognize the skin type precisely.

KEYWORDS

Cosmetic Skin type Machine learning Convolutional Neural Network.

Cite This Article

Suchethana H C, Himashree D, Keerthana Bhatt S R, Ranjitha G B, Smruthi S, "COSMETIC SUGGESTION BASED ON SKIN CONDITION," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 5, May 2024. https://doi.org/10.55041/IJSREM33511

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Publication Date

12/31/45420

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Double-Blind Peer Review

Review Rounds: 2

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5

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The authors declare no conflict of interest related to this research work.

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No funding information available for this research.

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