Mohammedkhan Hezha, Fleuren Hein, Güven Çíçek, Postma Eric
Department of Cognitive Science and Artificial Intelligence, School of Humanities and Digital Sciences, Tilburg University, 5037 AB Tilburg, The Netherlands.
Zero Hungerlab, Department of Econometrics and Operations Research, School of Economics and Management, Tilburg University, 5037 AB Tilburg, The Netherlands.
J Imaging. 2025 Jun 19;11(6):205. doi: 10.3390/jimaging11060205.
The prediction of anthropometric measurements from 2D body images, particularly for children, remains an under-explored area despite its potential applications in healthcare, fashion, and fitness. While pose estimation and body shape classification have garnered extensive attention, estimating body measurements and body mass index (BMI) from images presents unique challenges and opportunities. This paper provides a comprehensive review of the current methodologies, focusing on deep-learning approaches, both standalone and in combination with traditional machine-learning techniques, for inferring body measurements from facial and full-body images. We discuss the strengths and limitations of commonly used datasets, proposing the need for more inclusive and diverse collections to improve model performance. Our findings indicate that deep-learning models, especially when combined with traditional machine-learning techniques, offer the most accurate predictions. We further highlight the promise of vision transformers in advancing the field while stressing the importance of addressing model explainability. Finally, we evaluate the current state of the field, comparing recent results and focusing on the deviations from ground truth, ultimately providing recommendations for future research directions.
从二维人体图像预测人体测量数据，尤其是针对儿童的预测，尽管在医疗保健、时尚和健身领域有潜在应用，但仍是一个未被充分探索的领域。虽然姿势估计和身体形状分类已受到广泛关注，但从图像中估计身体测量数据和体重指数（BMI）带来了独特的挑战和机遇。本文对当前的方法进行了全面综述，重点关注深度学习方法，包括独立使用以及与传统机器学习技术相结合，用于从面部和全身图像推断身体测量数据。我们讨论了常用数据集的优缺点，提出需要更具包容性和多样性的数据集来提高模型性能。我们的研究结果表明，深度学习模型，特别是与传统机器学习技术相结合时，能提供最准确的预测。我们进一步强调了视觉Transformer在推动该领域发展方面的前景，同时强调了解决模型可解释性的重要性。最后，我们评估了该领域的现状，比较了近期结果并关注与真实值的偏差，最终为未来的研究方向提供了建议。
