{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T02:53:35Z","timestamp":1771296815354,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T00:00:00Z","timestamp":1745971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Zero Hunger Lab at Tilburg University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Precisely estimating a child\u2019s body measurements and weight from a single image is useful in pediatrics for monitoring growth and detecting early signs of malnutrition. The development of estimation models for this task is hampered by the unavailability of a labeled image dataset to support supervised learning. This paper introduces the \u201cAge-Restricted Anonymized\u201d (ARAN) dataset, the first labeled image dataset of children with body measurements approved by an ethics committee under the European General Data Protection Regulation guidelines. The ARAN dataset consists of images of 512 children aged 16 to 98 months, each captured from four different viewpoints, i.e., 2048 images in total. The dataset is anonymized manually on the spot through a face mask and includes each child\u2019s height, weight, age, waist circumference, and head circumference measurements. The dataset is a solid foundation for developing prediction models for various tasks related to these measurements; it addresses the gap in computer vision tasks related to body measurements as it is significantly larger than any other comparable dataset of children, along with diverse viewpoints. To create a suitable reference, we trained state-of-the-art deep learning algorithms on the ARAN dataset to predict body measurements from the images. The best results are obtained by a DenseNet121 model achieving competitive estimates for the body measurements, outperforming state-of-the-art results on similar tasks. The ARAN dataset is developed as part of a collaboration to create a mobile app to measure children\u2019s growth and detect early signs of malnutrition, contributing to the United Nations Sustainable Development Goals.<\/jats:p>","DOI":"10.3390\/jimaging11050142","type":"journal-article","created":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T05:10:32Z","timestamp":1746076232000},"page":"142","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["ARAN: Age-Restricted Anonymized Dataset of Children Images and Body Measurements"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7199-7286","authenticated-orcid":false,"given":"Hezha H.","family":"MohammedKhan","sequence":"first","affiliation":[{"name":"Zero Hunger Lab, Department of Econometrics & Operations Research, Tilburg School of Economics and Management, Tilburg University, 5037 AB Tilburg, The Netherlands"},{"name":"Department of Cognitive Science and Artificial Intelligence, Tilburg School of Humanities and Digital Sciences, Tilburg University, 5037 AB Tilburg, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5394-4489","authenticated-orcid":false,"given":"Cascha","family":"Van Wanrooij","sequence":"additional","affiliation":[{"name":"Zero Hunger Lab, Department of Econometrics & Operations Research, Tilburg School of Economics and Management, Tilburg University, 5037 AB Tilburg, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9627-1523","authenticated-orcid":false,"given":"Eric O.","family":"Postma","sequence":"additional","affiliation":[{"name":"Department of Cognitive Science and Artificial Intelligence, Tilburg School of Humanities and Digital Sciences, Tilburg University, 5037 AB Tilburg, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1939-8325","authenticated-orcid":false,"given":"\u00c7i\u00e7ek","family":"G\u00fcven","sequence":"additional","affiliation":[{"name":"Department of Cognitive Science and Artificial Intelligence, Tilburg School of Humanities and Digital Sciences, Tilburg University, 5037 AB Tilburg, The Netherlands"}]},{"given":"Marleen","family":"Balvert","sequence":"additional","affiliation":[{"name":"Zero Hunger Lab, Department of Econometrics & Operations Research, Tilburg School of Economics and Management, Tilburg University, 5037 AB Tilburg, The Netherlands"}]},{"given":"Heersh","family":"Raof Saeed","sequence":"additional","affiliation":[{"name":"College of Medicine, University of Sulaimani, Kurdistan Region, Sulaymaniyah 46002, Iraq"}]},{"given":"Chenar Omer","family":"Ali Al Jaf","sequence":"additional","affiliation":[{"name":"College of Medicine, University of Sulaimani, Kurdistan Region, Sulaymaniyah 46002, Iraq"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yousaf, N., Hussein, S., and Sultani, W. (2021). Estimation of BMI from facial images using semantic segmentation based region-aware pooling. Comput. Biol. Med., 133.","DOI":"10.1016\/j.compbiomed.2021.104392"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bhat, S.S., Ananth, A., Dsouza, P., Sharanyalaxmi, K. (2021). Human Body Measurement Extraction from 2D Images. Lecture Notes in Electrical Engineering, Springer.","DOI":"10.1007\/978-981-16-0443-0_28"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Choutas, V., M\u00fcller, L., Huang, C.H.P., Tang, S., Tzionas, D., and Black, M.J. (2022, January 18\u201324). Accurate 3D Body Shape Regression using Metric and Semantic Attributes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00274"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mocini, E., Cammarota, C., Frigerio, F., Muzzioli, L., Piciocchi, C., Lacalaprice, D., Buccolini, F., Donini, L.M., and Pinto, A. (2023). Digital anthropometry: A systematic review on precision, reliability and accuracy of most popular existing technologies. Nutrients, 15.","DOI":"10.3390\/nu15020302"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Gunel, S., Rhodin, H., and Fua, P. (2019, January 27\u201328). What face and body shapes can tell us about height. Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, Seoul, Republic of Korea.","DOI":"10.1109\/ICCVW.2019.00226"},{"key":"ref_6","unstructured":"UNICEF, WHO, and World Bank (2025, March 20). Joint Child Malnutrition Estimates (JME) 2023. The UNICEF, WHO, and the World Bank Inter-Agency Team Update the Joint Child Malnutrition Estimates (JME) Every Other Year. Available online: https:\/\/www.who.int\/data\/gho\/data\/themes\/topics\/joint-child-malnutrition-estimates-unicef-who-wb."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liu, Q., Li, C., Yang, L., Gong, Z., Zhao, M., Bovet, P., and Xi, B. (2024). Weight status change during four years and left ventricular hypertrophy in Chinese children. Front. Pediatr., 12.","DOI":"10.3389\/fped.2024.1371286"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Basahel, A.M., Bahbouh, N.M., Abi Sen, A.A., Yamin, M., and Basahel, M.A. (March, January 28). A Smart Way to Monitor Children Growth with the Help of ML. Proceedings of the 2024 11th International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India.","DOI":"10.23919\/INDIACom61295.2024.10499036"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Trivedi, A., Jain, M., Gupta, N.K., Hinsche, M., Singh, P., Matiaschek, M., Behrens, T., Militeri, M., Birge, C., and Kaushik, S. (2021, January 1\u20135). Height estimation of children under five years using depth images. Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Virtual.","DOI":"10.1109\/EMBC46164.2021.9630461"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"MohammedKhan, H., Balvert, M., Guven, C., and Postma, E. (2021, January 20\u201324). Predicting Human Body Dimensions from Single Images: A first step in automatic malnutrition detection. Proceedings of the CAIP 2021: Proceedings of the 1st International Conference on AI for People: Towards Sustainable AI, CAIP 2021, Bologna, Italy.","DOI":"10.4108\/eai.20-11-2021.2314166"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (2019, January 27\u201328). Searching for mobilenetv3. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_15","unstructured":"Ricanek, K., and Tesafaye, T. (2006, January 10\u201312). MORPH: A longitudinal image database of normal adult age-progression. Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition (FGR06), Southampton, UK."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2527","DOI":"10.1109\/TIFS.2019.2904840","article-title":"Body Weight Analysis From Human Body Images","volume":"14","author":"Jiang","year":"2019","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.patcog.2017.02.018","article-title":"Building Statistical Shape Spaces for 3D Human Modeling","volume":"67","author":"Pishchulin","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Robinette, K.M., Blackwell, S., Daanen, H., Boehmer, M., and Fleming, S. (2002). Civilian American and European Surface Anthropometry Resource (CAESAR), Final Report. Volume 1. Summary, Sytronics Inc.. Technical Report.","DOI":"10.21236\/ADA406704"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Andriluka, M., Pishchulin, L., Gehler, P., and Schiele, B. (2014, January 23\u201328). 2D Human Pose Estimation: New Benchmark and State of the Art Analysis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.471"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"248:1","DOI":"10.1145\/2816795.2818013","article-title":"SMPL: A Skinned Multi-Person Linear Model","volume":"34","author":"Loper","year":"2015","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hesse, N., Pujades, S., Romero, J., Black, M.J., Bodensteiner, C., Arens, M., Hofmann, U.G., Tacke, U., Hadders-Algra, M., and Weinberger, R. (2018, January 16\u201320). Learning an Infant Body Model from RGB-D Data for Accurate Full Body Motion Analysis. Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Granada, Spain.","DOI":"10.1007\/978-3-030-00928-1_89"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Patel, P., Huang, C.H., Tesch, J., Hoffmann, D.T., Tripathi, S., and Black, M.J. (2021, January 20\u201325). AGORA: Avatars in geography optimized for regression analysis. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01326"},{"key":"ref_24","unstructured":"Kim, K.H., Jones, M.L., Ebert, S.M., Malik, L., Manary, M.A., Reed, M.P., and Klinich, K.D. (2015). Development of Virtual Toddler Fit Models for Child Safety Restraint Design, University of Michigan, Ann Arbor, Transportation Research Institute. Technical Report."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"124879","DOI":"10.1016\/j.eswa.2024.124879","article-title":"A scale-equivariant CNN-based method for estimating human weight and height from multi-view clinic silhouette images","volume":"256","author":"Lima","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_26","unstructured":"Shah, C., Shah, J., Shaikh, M., Sandhu, H., and Natu, P. Anthropometric Measurement Technology Using 2D Images. Proceedings of the Second International Conference on Sustainable Expert Systems, Lecture Notes in Networks and Systems."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Park, D.H., Deng, J., Erhan, D., Rodriguez, C., and Anguelov, D. (2015, January 7\u201312). Going Deeper with Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_28","unstructured":"Yan, S., and K\u00e4m\u00e4r\u00e4inen, J.K. (2021). Learning anthropometry from rendered humans. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"\u0160korv\u00e1nkov\u00e1, D., Rie\u010dick\u00fd, A., and Madaras, M. (2022, January 6\u20138). Automatic Estimation of Anthropometric Human Body Measurements. Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Virtual.","DOI":"10.5220\/0010878100003124"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Dibra, E., Jain, H., Oztireli, C., Ziegler, R., and Gross, M. (2017, January 21\u201326). Human shape from silhouettes using generative hks descriptors and cross-modal neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.584"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kanazawa, A., Black, M.J., Jacobs, D.W., and Malik, J. (2018, January 18\u201323). End-to-end recovery of human shape and pose. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00744"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., and Black, M.J. Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image. Proceedings of the Computer Vision\u2014ECCV 2016; Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-319-46454-1_34"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020, January 23\u201328). End-to-end object detection with transformers. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_34","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet Large Scale Visual Recognition Challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis. (IJCV)"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"MohammedKhan, H., Guven, C., Balvert, M., and Postma, E. (2023, January 7\u20138). Image-Based Body Shape Estimation to Detect Malnutrition. Proceedings of the SAI Intelligent Systems Conference, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-031-47724-9_38"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/5\/142\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:25:05Z","timestamp":1760030705000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/5\/142"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,30]]},"references-count":36,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["jimaging11050142"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11050142","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,30]]}}}