  The scarcity of comprehensive datasets in surveillance, identification, image
retrieval systems, and healthcare poses a significant challenge for researchers
in exploring new methodologies and advancing knowledge in these respective
fields. Furthermore, the need for full-body image datasets with detailed
attributes like height, weight, age, and gender is particularly significant in
areas such as fashion industry analytics, ergonomic design assessment, virtual
reality avatar creation, and sports performance analysis. To address this gap,
we have created the 'Celeb-FBI' dataset which contains 7,211 full-body images
of individuals accompanied by detailed information on their height, age,
weight, and gender. Following the dataset creation, we proceed with the
preprocessing stages, including image cleaning, scaling, and the application of
Synthetic Minority Oversampling Technique (SMOTE). Subsequently, utilizing this
prepared dataset, we employed three deep learning approaches: Convolutional
Neural Network (CNN), 50-layer ResNet, and 16-layer VGG, which are used for
estimating height, weight, age, and gender from human full-body images. From
the results obtained, ResNet-50 performed best for the system with an accuracy
rate of 79.18% for age, 95.43% for gender, 85.60% for height and 81.91% for
weight.
