Systematic Review of AI-Based Approaches for Anthropometric and Fashion Landmark Detection in Body Measurement Estimation

Abstract

This systematic review gives a detailed discussion of anthropometric landmark abstraction and dimension measurement techniques with a focus on their use in the Fashion and Apparel (F&A) industry. It starts with an overview of the leading object detection models and their application in the detection of garments and human body features. The review makes a clear distinction between fashion landmark detection, which aims at detecting key points on clothing, and anthropometric landmark detection, which isolates anatomical landmarks on the human body to obtain measurement estimates. Different measurement extraction techniques are addressed, which include 2D silhouette analysis, 3D body scanning, and mesh-based modeling to acquire standardized anthropometric parameters, which include lengths, breadths, depths, and circumferences. The originality of this review is that it is the first analytical framework that combines the two domains of anthropometric and fashion landmark detection that have been historically examined separately. The review fills the gap between the human body measurement estimation and clothing landmark abstraction by providing a cross-domain synthesis, which provides a unified view of algorithms, datasets, and evaluation metrics. Moreover, it compares new methods including classical machine learning methods and modern deep learning and ensemble methods, demonstrating the performance increase depending on the accuracy metrics like Mean Absolute Error (MAE) and Normalized Error (NE). The review identifies a clear research direction that is moving away the conventional computer vision pipelines to the data-driven deep learning solutions. In general, the review provides new knowledge that can be used to develop garment fit prediction, virtual try-on technologies, and intelligent apparel recommendation systems by incorporating anthropometric and fashion-based landmark detection strategies.