通过评估面部光照质量提升美妆产品推荐的准确性
Improving the Accuracy of Beauty Product Recommendations by Assessing Face Illumination Quality
AI总结：
针对美妆产品推荐中面部照片光照条件差导致推荐质量下降的问题，提出基于CNN的光照评估框架，构建多样化合成数据集训练模型并开发用户引导工具，有效提升了粉底等产品的色号推荐准确性。
摘要：
我们致力于解决负责任的美妆产品推荐中的挑战，特别是当涉及将产品颜色与人的肤色进行比较时，例如粉底和遮瑕膏产品。为了做出准确的推荐，推断产品属性以及特定于产品的面部特征（如皮肤状况或色调）至关重要。然而，虽然许多产品照片是在良好的光照条件下拍摄的，但面部照片的拍摄条件却千差万别。使用光照不良环境下的照片提取的特征可能会产生高度误导，甚至无法与产品属性进行比较。因此，糟糕的光照条件会严重降低推荐质量。我们引入了一个用于光照评估的机器学习框架，该框架将图像分类为光照条件良好或光照条件糟糕。然后，我们构建了一个自动用户引导工具，用于告知拿着相机的用户其光照条件是好是坏。通过这种方式，用户可以获得快速反馈，并能够交互式地控制如何拍摄照片以进行推荐。只有少数研究致力于这一问题，主要是由于缺乏在肤色和光照模式方面均具有规模、带标签且多样化的数据集。缺乏此类数据集导致忽略了肤色的多样性。因此，我们首先构建了一个多样化的合成数据集，该数据集在现有面部图像数据集的基础上，模拟了各种肤色和光照模式。接下来，我们训练了一个卷积神经网络（CNN）用于光照评估，使用该合成数据集的表现优于现有解决方案。最后，我们分析了我们的工作如何改善各种粉底产品的色号推荐。
英文摘要：
We focus on addressing the challenges in responsible beauty product recommendation, particularly when it involves comparing the product's color with a person's skin tone, such as for foundation and concealer products. To make accurate recommendations, it is crucial to infer both the product attributes and the product specific facial features such as skin conditions or tone. However, while many product photos are taken under good light conditions, face photos are taken from a wide range of conditions. The features extracted using the photos from ill-illuminated environment can be highly misleading or even be incompatible to be compared with the product attributes. Hence bad illumination condition can severely degrade quality of the recommendation. We introduce a machine learning framework for illumination assessment which classifies images into having either good or bad illumination condition. We then build an automatic user guidance tool which informs a user holding their camera if their illumination condition is good or bad. This way, the user is provided with rapid feedback and can interactively control how the photo is taken for their recommendation. Only a few studies are dedicated to this problem, mostly due to the lack of dataset that is large, labeled, and diverse both in terms of skin tones and light patterns. Lack of such dataset leads to neglecting skin tone diversity. Therefore, We begin by constructing a diverse synthetic dataset that simulates various skin tones and light patterns in addition to an existing facial image dataset. Next, we train a Convolutional Neural Network (CNN) for illumination assessment that outperforms the existing solutions using the synthetic dataset. Finally, we analyze how the our work improves the shade recommendation for various foundation products.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
