Abstract
This study presents a robust framework that leverages advanced imaging techniques and machine learning for feature extraction and classification of key human attributes-namely skin tone, hair color, iris color, and vein-based undertones. The system employs a multi-stage pipeline involving face detection, region segmentation, and dominant color extraction to isolate and analyze these features. Techniques such as X-means clustering, alongside perceptually uniform distance metrics like Delta E (CIEDE2000), are applied within both LAB and HSV color spaces to enhance the accuracy of color differentiation. For classification, the dominant tones of the skin, hair, and iris are extracted and matched to a custom tone scale, while vein analysis from wrist images enables undertone classification into "Warm" or "Cool" based on LAB differences. Each module uses targeted segmentation and color space transformations to ensure perceptual precision. The system achieves up to 80% accuracy in tone classification using the Delta E-HSV method with Gaussian blur, demonstrating reliable performance across varied lighting and image conditions. This work highlights the potential of AI-powered color analysis and feature extraction for delivering inclusive, precise, and nuanced classification, supporting applications in beauty technology, digital personalization, and visual analytics.
Abstract (translated)
这项研究提出了一种利用先进成像技术和机器学习进行特征提取和分类的关键人类属性框架，这些关键属性包括肤色、发色、虹膜颜色以及基于静脉的底色调。系统采用多阶段管道，涉及面部检测、区域分割和主色调提取来分离并分析这些特征。 研究中采用了X-means聚类等技术，并在LAB和HSV色彩空间内应用感知一致的距离度量（如CIEDE2000中的Delta E），以提高颜色区分的准确性。对于分类而言，从皮肤、头发和虹膜中提取主要色调并与自定义色调等级匹配，同时手腕图像中的静脉分析基于LAB差异将底色分类为“暖”或“冷”。每个模块使用目标分割和色彩空间转换来确保感知精度。 该系统在使用Delta E-HSV方法并结合高斯模糊的情况下，在色调分类上实现了高达80%的准确率，展示了其在不同光照和图像条件下可靠的表现力。这项工作突显了AI驱动的颜色分析和特征提取在美容技术、数字个性化以及视觉分析中的潜力，可以实现包容性、精确且细致的分类支持。
URL
https://arxiv.org/abs/2505.14931
