DOI: 10.5281/zenodo.14474899
title: SoloFace: A Single-Face Dataset for Resource-Constrained Face Detection and Tracking

description:  

SoloFace: A Single-Face Dataset for Resource-Constrained Face Detection and Tracking

DescriptionSoloFace is a custom dataset derived from the COCO-Faces and Visual Wake Word datasets, specifically designed for single-face detection tasks in resource-constrained environments. This dataset is ideal for developing machine learning models for embedded AI applications, such as TinyML, which operate on low-power devices. Each image either contains a single human face or no face, with corresponding labels providing class information and bounding box coordinates for face detection. The dataset includes data augmentation to ensure robustness across diverse conditions, such as variations in lighting, scale, and orientation.



Dataset StructureThe dataset is organized into three subsets: train, test, and val. Each subset contains:



images/: .jpg image files.

labels/: .json label files with matching filenames to the images.


Label FormatEach .json label file includes:



image: Name of the corresponding image file.

class: 1 if a face is present, 0 otherwise.

bbox: Normalized bounding box coordinates [top_left_x, top_left_y, bottom_right_x, bottom_right_y]. If no face is present, the bounding box is set to [0.0, 0.0, 0.01, 0.01].




Statistics





Original Dataset:



Training images: 11,272

Testing images: 3,732

Validation images: 434





After Data Augmentation:



Training images: 56,360

Testing and validation images remain unchanged.





Class Distribution:



50% of images contain a single visible human face.

50% contain no human face.






Data Augmentation DetailsTo improve model robustness, the following augmentation techniques were applied to the training set:



Geometric Transformations: Random rotation (±15 degrees), scaling (±20%), and horizontal flipping (50%).

Color Transformations: Brightness and contrast adjustments (±30%).

Cropping: Random cropping up to 10% from image edges.


Each augmentation preserved bounding box consistency with the transformed images.



Usage This dataset supports the following use cases:



Training lightweight face detection models optimized for microcontroller deployment.

Benchmarking single-face detection models in resource-constrained environments.

Research on model robustness and efficiency.




Loading the Dataset



Download the dataset.

Extract the dataset using:

unzip soloface-detection-dataset.zip



Dataset structure:

soloface-detection-dataset/
├── train/
│   ├── images/
│   ├── labels/
├── test/
│   ├── images/
│   ├── labels/
├── val/
│   ├── images/
│   ├── labels/






LicenseThis dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.



Permissions: Copy, distribute, and adapt for any purpose, including commercial.

Conditions: Provide proper attribution, a link to the license, and indicate changes.

Restrictions: No additional legal or technological restrictions.


For more details, visit the CC BY 4.0 License.



ContactFor inquiries or collaborations, please contact:



Bidyut Saha: sahabidyut999@gmail.com

Riya Samanta: study.riya1792@gmail.com




This format fits Zenodo's description field requirements while providing clarity and structure. Let me know if further refinements are needed!