Intrinsic Shape LDA With Application to Body Human Shapes Classification

ABSTRACT

Advancements in 3D scanning and cloud infrastructure enable the acquisition and analysis of high‐density body surface datasets. In this work, we propose a novel methodology that extends linear discriminant analysis (LDA) to Kendall's shape space for the classification of 3D objects, specifically human body shapes. Our approach adapts LDA to the non‐Euclidean geometry of shape space, generalizing Euclidean distributional assumptions and incorporating parallel transport to improve the estimation of inter‐cluster variability. Through several simulation studies, we demonstrate the effectiveness of the proposed methodology before applying it to classify female body shapes across various age ranges.