Celeb-FBI: Celebrity Full Body Images Dataset

A cleaned and restructured version of the Celeb-FBI dataset containing 7,208 full-body celebrity images with annotations for height, weight, age, and gender.

Dataset Description

This dataset consists of worldwide celebrity images captured in standing, front-facing positions. It is designed for research on human attribute estimation from full-body images, including height, weight, age, and gender prediction tasks.

Dataset Structure

DatasetDict({
    train: Dataset({
        features: ['id', 'image', 'height', 'weight', 'gender', 'age'],
        num_rows: 6487
    })
    test: Dataset({
        features: ['id', 'image', 'height', 'weight', 'gender', 'age'],
        num_rows: 721
    })
})

Features

Feature	Type	Description
id	int	Unique identifier for the image
image	Image	Full-body celebrity photograph
height	float	Height in centimeters (-1 if missing/invalid)
weight	float	Weight in kilograms (-1 if missing/invalid)
gender	int	0 = Male, 1 = Female
age	int	Age in years (-1 if missing/invalid)

Statistics

Attribute	Min	Max	Mean	Valid Samples
Height	79 cm	259 cm	170 cm	~6,100
Weight	38 kg	202 kg	66 kg	~5,300
Age	14	97	42	~6,500
Gender	—	—	61% F	7,208

Data Processing

This version of the dataset includes several improvements over the original:

Cleaning steps applied:

• Converted height from feet to centimeters for standardization
• Removed implausible values (e.g., heights outside reasonable human range)
• Missing or invalid values are encoded as -1
• Fixed typos in original annotations
• Manual corrections for identified mislabeled samples

Train/test split:

• Stratified 90/10 split based on height, age, weight buckets, and gender
• Ensures balanced representation across attribute combinations

Note: Approximately 14% of samples have at least one missing or invalid attribute value (marked as -1). The dataset contains some noise in annotations—users should account for this in their applications.

Usage

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("alecccdd/celeb-fbi")

# Access training data
train_data = dataset["train"]

# Example: iterate over samples
for sample in train_data:
    image = sample["image"]
    height = sample["height"]  # in cm, -1 if missing
    weight = sample["weight"]  # in kg, -1 if missing
    gender = sample["gender"]  # 0=male, 1=female
    age = sample["age"]        # -1 if missing
    
# Filter valid samples for a specific attribute
valid_height_samples = train_data.filter(lambda x: x["height"] != -1)

Intended Uses

• Human attribute estimation research (height, weight, age, gender)
• Multi-task learning on human body images
• Benchmarking computer vision models for biometric prediction
• Study of visual cues for physical attribute estimation

Limitations

• Images are of celebrities and may not represent the general population
• Annotation accuracy depends on publicly available biographical data
• Some noise exists in the annotations; manual corrections were applied where identified but the dataset is not exhaustively verified
• Limited age range representation at extremes (few samples under 20 or over 80)
• Height and weight distributions may reflect celebrity demographics

Ethical Considerations

This dataset uses publicly available images of celebrities. Users should be mindful of:

• Privacy implications when developing attribute estimation systems
• Potential biases in celebrity image datasets
• Responsible use in downstream applications

Citation

If you use this dataset, please cite the original paper:

@misc{debnath2024celebfbibenchmarkdatasethuman,
      title={Celeb-FBI: A Benchmark Dataset on Human Full Body Images and Age, Gender, Height and Weight Estimation using Deep Learning Approach}, 
      author={Pronay Debnath and Usafa Akther Rifa and Busra Kamal Rafa and Ali Haider Talukder Akib and Md. Aminur Rahman},
      year={2024},
      eprint={2407.03486},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2407.03486}, 
}

Paper: arXiv:2407.03486