用机器学习量化掌纹特征，预测外部特质
Palmistry-Informed Feature Extraction and Analysis using Machine Learning
- 构建视觉流水线提取掌纹结构、纹理和形状特征
- 在新标注数据集上实现掌纹与外部特征的关联预测
- 适合对生物识别或数字人体测量感兴趣的研究者
本文探索利用机器学习技术实现掌纹特征的自动化分析。我们提出一种计算机视觉流程，从掌纹图像中提取主纹线结构、纹理和形状度量等关键特征，并基于全新标注的掌纹图像数据集训练预测模型。该方法突破传统主观解读局限，提供数据驱动、量化的框架，用于研究掌纹形态与外部验证特征或状态之间的关联。方法在数字人体测量和个性化用户分析中展现可行性，具备移动端部署潜力。结果表明，机器学习模型可识别掌纹数据中的复杂模式，为文化实践与计算分析交叉研究开辟新路径。
原文摘要 · Abstract (English)
This paper explores the automated analysis of palmar features using machine learning techniques. We present a computer vision pipeline that extracts key characteristics from palm images, such as principal line structures, texture, and shape metrics. These features are used to train predictive models on a novel dataset curated from annotated palm images. Our approach moves beyond traditional subjective interpretation by providing a data-driven, quantitative framework for studying the correlations between palmar morphology and externally validated traits or conditions. The methodology demonstrates feasibility for applications in digital anthropometry and personalized user analytics, with potential for deployment on mobile platforms. Results indicate that machine learning models can identify complex patterns in palm data, opening avenues for research that intersects cultural practices with computational analysis.
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