An AI-Assisted Skincare Routine Recommendation System in XR
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913274538229760 |
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| author | Rajegowda, Gowravi Malalur Spyridis, Yannis Villarini, Barbara Argyriou, Vasileios |
| author_facet | Rajegowda, Gowravi Malalur Spyridis, Yannis Villarini, Barbara Argyriou, Vasileios |
| contents | In recent years, there has been an increasing interest in the use of artificial intelligence (AI) and extended reality (XR) in the beauty industry. In this paper, we present an AI-assisted skin care recommendation system integrated into an XR platform. The system uses a convolutional neural network (CNN) to analyse an individual's skin type and recommend personalised skin care products in an immersive and interactive manner. Our methodology involves collecting data from individuals through a questionnaire and conducting skin analysis using a provided facial image in an immersive environment. This data is then used to train the CNN model, which recognises the skin type and existing issues and allows the recommendation engine to suggest personalised skin care products. We evaluate our system in terms of the accuracy of the CNN model, which achieves an average score of 93% in correctly classifying existing skin issues. Being integrated into an XR system, this approach has the potential to significantly enhance the beauty industry by providing immersive and engaging experiences to users, leading to more efficient and consistent skincare routines. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_13466 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An AI-Assisted Skincare Routine Recommendation System in XR Rajegowda, Gowravi Malalur Spyridis, Yannis Villarini, Barbara Argyriou, Vasileios Computer Vision and Pattern Recognition In recent years, there has been an increasing interest in the use of artificial intelligence (AI) and extended reality (XR) in the beauty industry. In this paper, we present an AI-assisted skin care recommendation system integrated into an XR platform. The system uses a convolutional neural network (CNN) to analyse an individual's skin type and recommend personalised skin care products in an immersive and interactive manner. Our methodology involves collecting data from individuals through a questionnaire and conducting skin analysis using a provided facial image in an immersive environment. This data is then used to train the CNN model, which recognises the skin type and existing issues and allows the recommendation engine to suggest personalised skin care products. We evaluate our system in terms of the accuracy of the CNN model, which achieves an average score of 93% in correctly classifying existing skin issues. Being integrated into an XR system, this approach has the potential to significantly enhance the beauty industry by providing immersive and engaging experiences to users, leading to more efficient and consistent skincare routines. |
| title | An AI-Assisted Skincare Routine Recommendation System in XR |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.13466 |