Palmistry-Informed Feature Extraction and Analysis using Machine Learning

Abstract

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.