A Deep Learning Framework for Personalized Fashion Recommendations Based on Skin Tone Analysis

H. P. Dissanayake and J. M. Chan Sri Manukalpa
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H. P. Dissanayake: Department of Physical Science, Rajarata University of Sri Lanka
J. M. Chan Sri Manukalpa: Department of Information Technology, SLIIT City Uni, Colombo 03, Sri Lanka

International Journal of Latest Technology in Engineering, Management & Applied Science, 2025, vol. 14, issue 6, 1012-1019

Abstract: The personalization of virtual fashion recommendations remains hindered by limited integration of chromatic and anthropometric factors, especially skin tone compatibility. This study addresses a critical research gap by proposing a voice-enabled 3D fashion recommendation system that incorporates deep learning-based skin tone classification and adaptive garment suggestion. The primary objective is to enhance aesthetic compatibility and user satisfaction through real-time, personalized recommendations. Utilizing a custom-designed Deep Convolutional Neural Network (DCNN) and reinforcement learning algorithms, the system classifies user skin tones with 89.14% accuracy and adapts recommendations based on user feedback, reducing outfit resets by 54%. A curated dataset encompassing five skin tone categories and a multi-stage image preprocessing pipeline ensures inclusive and robust performance. The results demonstrate significant improvements in recommendation relevance and user engagement, with 88% satisfaction and 93.7% dominant tone detection accuracy. These findings underscore the system's potential to set new benchmarks in personalized fashion retail while promoting inclusivity and sustainable consumer practices.

Date: 2025
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