Abstract
Estimating an individual's height from a two-dimensional (2D) image has emerged as a focal point of investigation within Computer Vision. Notably, the research landscape has witnessed endeavors dedicated to human height estimation from frontal-face and full-body images. Nevertheless, there exists an imperative need for further exploration within this domain, primarily aimed at enhancing the precision of such estimations. Within this context, our study seeks to delve into the nuanced task of height from a solitary full-body image. Leveraging cutting-edge AI methodologies, we have harnessed the capabilities of Yolov7 for human pose estimation, alongside the DeepFace pre-trained model for age, gender, and race estimation, to serve as pivotal feature extraction mechanisms. Notably, our model's efficacy is intrinsically linked to the performance benchmarks set by these two foundational models. Our empirical observations underscore the commendable performance of Yolov7; however, it is incumbent to acknowledge that the DeepFace model has, regrettably, not demonstrated commensurate levels of accuracy in our experimentation. In the present study, we have employed a set of regression and tree-based predictive models, including Linear Regression, AdaBoost Regressor, Decision Tree, and Random Forest. Notably, the AdaBoost Regressor model has exhibited superior performance compared to the other models under consideration. Specifically, it has demonstrated a remarkable mean absolute error of 6.2 cms and a mean squared error of 7.9 cms, thereby establishing its efficacy in minimizing prediction errors in this context.