TY - GEN
T1 - An AI-Powered Digital Foundation Recommender System
AU - Ruiz, D. M.
AU - Watson, A.
AU - Kumar, Y.
AU - Li, J. J.
AU - Morreale, P.
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This paper addresses the significant challenge of selecting suitable foundation shades, particularly for darker skin tones, which have been inadequately represented and catered to in the beauty industry. It introduces an innovative system designed to offer individualized makeup recommendations, significantly diminishing the time and effort traditionally demanded of consumers when searching for appropriate products. Utilizing machine learning methodologies and computer vision techniques, the system analyzes image data to precisely identify a range of skin tones, enabling it to propose compatible foundation shades automatically. Unlike sophisticated Large Language Models, such as ChatGPT, Gemini, Microsoft Copilot, or Claude, which are not equipped to undertake such visually driven tasks due to ethical guidelines that prohibit them from processing personal images without explicit consent and the absence of face image processing capabilities, this technological development represents a step forward in fostering inclusivity, illustrating the transformative potential of AI in accommodating the unique beauty preferences of all individuals.
AB - This paper addresses the significant challenge of selecting suitable foundation shades, particularly for darker skin tones, which have been inadequately represented and catered to in the beauty industry. It introduces an innovative system designed to offer individualized makeup recommendations, significantly diminishing the time and effort traditionally demanded of consumers when searching for appropriate products. Utilizing machine learning methodologies and computer vision techniques, the system analyzes image data to precisely identify a range of skin tones, enabling it to propose compatible foundation shades automatically. Unlike sophisticated Large Language Models, such as ChatGPT, Gemini, Microsoft Copilot, or Claude, which are not equipped to undertake such visually driven tasks due to ethical guidelines that prohibit them from processing personal images without explicit consent and the absence of face image processing capabilities, this technological development represents a step forward in fostering inclusivity, illustrating the transformative potential of AI in accommodating the unique beauty preferences of all individuals.
KW - AI-based shade matching
KW - computer vision for foundation matching
KW - inclusivity in cosmetics
KW - personalized foundation shade recommender
KW - skin tone classification
UR - http://www.scopus.com/inward/record.url?scp=85212205812&partnerID=8YFLogxK
U2 - 10.1109/ISNCC62547.2024.10759049
DO - 10.1109/ISNCC62547.2024.10759049
M3 - Conference contribution
AN - SCOPUS:85212205812
T3 - 2024 International Symposium on Networks, Computers and Communications, ISNCC 2024
BT - 2024 International Symposium on Networks, Computers and Communications, ISNCC 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 International Symposium on Networks, Computers and Communications, ISNCC 2024
Y2 - 22 October 2024 through 25 October 2024
ER -