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Personal styling boosts confidence and looks. But, professional services are still costly and unavailable. This research paper presents a dual-component platform designed to provide personalized hairstyle and clothing color recommendations. The first component utilizes a deep learning model based on MobileNetV2 to analyse facial images and recommend suitable clothing colors by extracting facial skin tone. The second component employs CNN for facial shape analysis to suggest compatible hairstyles, enhancing user confidence and aesthetic appeal. Our system leverages image processing, feature extraction, and advanced neural network architectures to deliver precise and user-specific outputs. This approach aims to bridge the gap between professional styling services and everyday consumers, making personalized fashion advice more accessible and efficient, offering a seamless and interactive user experience.

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Color Analysis and Hairstyle Recommendation System Using MobilenetV2 and CNN

  • 会议论文

  • 发表日期：

    2026年

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  • Swathi Priyadarshini Tigulla,
  • Jhansi Sai Sree Chandaluri,
  • Alekhya Boju,
  • Rineesha Saginala

摘要

Personal styling boosts confidence and looks. But, professional services are still costly and unavailable. This research paper presents a dual-component platform designed to provide personalized hairstyle and clothing color recommendations. The first component utilizes a deep learning model based on MobileNetV2 to analyse facial images and recommend suitable clothing colors by extracting facial skin tone. The second component employs CNN for facial shape analysis to suggest compatible hairstyles, enhancing user confidence and aesthetic appeal. Our system leverages image processing, feature extraction, and advanced neural network architectures to deliver precise and user-specific outputs. This approach aims to bridge the gap between professional styling services and everyday consumers, making personalized fashion advice more accessible and efficient, offering a seamless and interactive user experience.