Celeb-FBI: A Benchmark Dataset on Human Full Body Images and Age, Gender, Height and Weight Estimation using Deep Learning Approach

• URL: http://arxiv.org/abs/2407.03486v1
• Date: Wed, 3 Jul 2024 20:16:47 GMT
• Title: Celeb-FBI: A Benchmark Dataset on Human Full Body Images and Age, Gender, Height and Weight Estimation using Deep Learning Approach
• Authors: Pronay Debnath, Usafa Akther Rifa, Busra Kamal Rafa, Ali Haider Talukder Akib, Md. Aminur Rahman,
• Abstract summary: 'Celeb-FBI' dataset contains 7,211 full-body images of individuals accompanied by detailed information on their height, age, weight, and gender. We employ three deep learning approaches: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• SDFD: Building a Versatile Synthetic Face Image Dataset with Diverse Attributes [14.966767182001755]
  We propose a methodology for generating synthetic face image datasets that capture a broader spectrum of facial diversity. Specifically, our approach integrates demographics and biometrics but also non-permanent traits like make-up, hairstyle, and accessories. These prompts guide a state-of-the-art text-to-image model in generating a comprehensive dataset of high-quality realistic images.
  arXiv  Detail & Related papers  (2024-04-26T08:51:31Z)
• Data Augmentation in Human-Centric Vision [54.97327269866757]
  This survey presents a comprehensive analysis of data augmentation techniques in human-centric vision tasks. It delves into a wide range of research areas including person ReID, human parsing, human pose estimation, and pedestrian detection. Our work categorizes data augmentation methods into two main types: data generation and data perturbation.
  arXiv  Detail & Related papers  (2024-03-13T16:05:18Z)
• Whole-body Detection, Recognition and Identification at Altitude and Range [57.445372305202405]
  We propose an end-to-end system evaluated on diverse datasets. Our approach involves pre-training the detector on common image datasets and fine-tuning it on BRIAR's complex videos and images. We conduct thorough evaluations under various conditions, such as different ranges and angles in indoor, outdoor, and aerial scenarios.
  arXiv  Detail & Related papers  (2023-11-09T20:20:23Z)
• Self-Supervised Masked Digital Elevation Models Encoding for Low-Resource Downstream Tasks [0.6374763930914523]
  GeoAI is uniquely poised to take advantage of the self-supervised methodology due to the decades of data collected. The proposed architecture is the Masked Autoencoder pre-trained on ImageNet.
  arXiv  Detail & Related papers  (2023-09-06T21:20:10Z)
• CongNaMul: A Dataset for Advanced Image Processing of Soybean Sprouts [0.26786930391553554]
  CongNaMul dataset is curated to facilitate tasks such as image classification, semantic segmentation, decomposition, and measurement of length and weight. The classification task provides four classes to determine the quality of soybean sprouts: normal, broken, spotted, and broken and spotted. The dataset is expected to be a valuable resource for a wide range of research and applications in the advanced analysis of images of soybean sprouts.
  arXiv  Detail & Related papers  (2023-08-30T01:14:32Z)
• One-Shot Learning for Periocular Recognition: Exploring the Effect of Domain Adaptation and Data Bias on Deep Representations [59.17685450892182]
  We investigate the behavior of deep representations in widely used CNN models under extreme data scarcity for One-Shot periocular recognition. We improved state-of-the-art results that made use of networks trained with biometric datasets with millions of images. Traditional algorithms like SIFT can outperform CNNs in situations with limited data.
  arXiv  Detail & Related papers  (2023-07-11T09:10:16Z)
• Learning Customized Visual Models with Retrieval-Augmented Knowledge [104.05456849611895]
  We propose REACT, a framework to acquire the relevant web knowledge to build customized visual models for target domains. We retrieve the most relevant image-text pairs from the web-scale database as external knowledge, and propose to customize the model by only training new modualized blocks while freezing all the original weights. The effectiveness of REACT is demonstrated via extensive experiments on classification, retrieval, detection and segmentation tasks, including zero, few, and full-shot settings.
  arXiv  Detail & Related papers  (2023-01-17T18:59:06Z)
• StyleGAN-Human: A Data-Centric Odyssey of Human Generation [96.7080874757475]
  This work takes a data-centric perspective and investigates multiple critical aspects in "data engineering" We collect and annotate a large-scale human image dataset with over 230K samples capturing diverse poses and textures. We rigorously investigate three essential factors in data engineering for StyleGAN-based human generation, namely data size, data distribution, and data alignment.
  arXiv  Detail & Related papers  (2022-04-25T17:55:08Z)
• Applying Artificial Intelligence for Age Estimation in Digital Forensic Investigations [0.8122270502556371]
  Investigators often need to determine the age of victims by looking at images and interpreting the sexual development stages and other human characteristics. This paper evaluates existing facial image datasets and proposes a new dataset tailored to the needs of similar digital forensic research contributions. The new dataset is tested on the Deep EXpectation (DEX) algorithm pre-trained on the IMDB-WIKI dataset.
  arXiv  Detail & Related papers  (2022-01-09T16:25:37Z)
• Deep learning for identification and face, gender, expression recognition under constraints [1.2647816797166165]
  Deep convolutional neural network (CNN) is used in this work to extract the features from veiled-person face images. The main objective of this work is to test the ability of deep learning based automated computer system to identify not only persons, but also to perform recognition of gender, age, and facial expressions such as eye smile.
  arXiv  Detail & Related papers  (2021-11-02T22:45:09Z)
• Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training Samples [52.549928980694695]
  In situ hybridization (ISH) gene expression pattern images from the same developmental stage are compared. labeling training data with precise stages is very time-consuming even for biologists. We propose a deep two-step low-shot learning framework to accurately classify ISH images using limited training images.
  arXiv  Detail & Related papers  (2020-10-20T06:06:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.