Abstract
The detection of malnutrition in children contributes to the United Nations’ second Sustainable Development Goal (SDG2): Zero Hunger. One of SDG2’s indicators is the prevalence of malnutrition among children under the age of five. Certain body measures such as stature (height) and head circumference are typically used to assess growth and malnutrition in children. In this paper we examine the feasibility of using convolutional neural networks (CNNs) to infer body shape directly from images. We aim to (i) predict three body measurements: height, head circumference and waist circumference, and, (ii) using a parameterised body model, predict the body-shape parameters from images. We created a multi-view collection of images of human bodies based on the CAESAR and AGORA datasets. Our predictions of the three body measurements are competitive with those obtained in a recent study for stature and head circumference, but not for waist circumference. Our predictions of the body-shape parameters, yields reasonable estimates of the body shape parameters, that seem to be hampered by pose and size variations. Our findings lead us to conclude that image-based assessment of body shape seems feasible. Further work is needed to assess the potential of parameterised body models and the generalisation to in-the-wild assessment of child malnourishment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bogo, F., Kanazawa, A., Lassner, C., Gehler, P.V., Romero, J., Black, M.J.: Keep it SMPL: automatic estimation of 3D human pose and shape from a single image. In: European Conference on Computer Vision (ECCV2016) (2016)
Ferrari, C., Casini, L., Berretti, S., Del Bimbo, A.: Monocular 3D body shape reconstruction under clothing. J. Imaging 7, 257 (2021)
Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261–2269. IEEE Computer Society, Los Alamitos, CA, USA (2017)
Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M.J.: SMPL: a skinned multi-person linear model. ACM Trans. Graph. (TOG) 34(6), 1–16 (2015)
Madadi, M., Bertiche, H., Escalera, S.: SMPLR: deep learning based SMPL reverse for 3D human pose and shape recovery. Pattern Recogn. 106, 107472 (2020)
Mohammedkhan, H., Balvert, M., Güven, Ç., Postma, E.: Predicting human body dimensions from single images: a first step in automatic malnutrition detection. In: Proceedings of International Conference on AI for People: towards Sustainable AI, CAIP’21 (2021)
Patel, P., Huang, C.-H.P., Tesch, J., Hoffmann, D.T., Tripathi, S., Black, M.J.: AGORA: avatars in geography optimized for regression analysis. In: Proceedings IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
Pavlakos, G., Choutas, V., Ghorbani, N., Bolkart, T., Osman, A.A., Tzionas, D., Black, M.J.: Expressive body capture: 3D hands, face, and body from a single image. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10975–10985 (2019)
Pishchulin, L., Wuhrer, S., Helten, T., Theobalt, C., Schiele, B.: Building statistical shape spaces for 3D human modeling. Pattern Recogn. 67, 276–286 (2017)
Robinette, K.M., Blackwell, S., Daanen, H., Boehmer, M., Fleming, S.: Civilian American and European surface anthropometry resource (CAESAR), final report. volume 1. Summary. Technical report, Systronics Inc., Dayton OH (2002)
Rong, Y., Shiratori, T., Joo, H.: FrankMocap: a monocular 3D whole-body pose estimation system via regression and integration. In: IEEE International Conference on Computer Vision Workshops (2021)
Yan, S., Kämäräinen, J.-K.: Learning anthropometry from rendered humans (2021). arxiv:2101.02515
Yang, Y., Yu, Y., Zhou, Y., Du, S., Davis, J., Yang, R.: Semantic parametric reshaping of human body models. In: 2nd International Conference on 3D Vision, vol. 2, pp. 41–48. IEEE (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
MohammedKhan, H., Guven, C., Balvert, M., Postma, E. (2024). Image-Based Body Shape Estimation to Detect Malnutrition. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2023. Lecture Notes in Networks and Systems, vol 823. Springer, Cham. https://doi.org/10.1007/978-3-031-47724-9_38
Download citation
DOI: https://doi.org/10.1007/978-3-031-47724-9_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-47723-2
Online ISBN: 978-3-031-47724-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)