Introduction

Welcome to the South Asian Human Face with Occlusion Dataset, carefully curated to support the development of robust facial recognition systems, occlusion detection models, biometric identification technologies, and KYC verification tools. This dataset provides real-world variability by including facial images with common occlusions, helping AI models perform reliably under challenging conditions.

Facial Image Data

The dataset comprises over 5,000 high-quality facial images, organized into participant-wise sets. Each set includes:

Diversity & Representation

Image Quality & Capture Conditions

To ensure robustness and real-world utility, images were captured under diverse conditions:

Metadata

Each image is paired with detailed metadata to enable advanced filtering, model tuning, and analysis:

This rich metadata helps train models that can recognize faces even when partially obscured.

Use Cases & Applications

This dataset is ideal for a wide range of real-world and research-focused applications, including:

Secure & Ethical Collection

Hello everyone , this is a dataset I am sharing , contains Happy and Non-Happy facial expressions to practice binary classification It contains labelled images of happy facial expression . I found this dataset while learning on coursera and I'd like to acknowledge them as the primary owner of the dataset

Instagram Faces Image dataset with diverse single-face images for facial recognition, anti-spoofing, and computer vision

This data is used in the second experimental evaluation of face smile detection in the paper titled "Smile detection using Hybrid Face Representaion" - O.A.Arigbabu et al. 2015.

Download the main images from LFWcrop website: http://conradsanderson.id.au/lfwcrop/ to select the samples we used for smile and non-smile, as in the list.

Kindly cite:

Arigbabu, Olasimbo Ayodeji, et al. "Smile detection using hybrid face representation." Journal of Ambient Intelligence and Humanized Computing (2016): 1-12.

C. Sanderson, B.C. Lovell. Multi-Region Probabilistic Histograms for Robust and Scalable Identity Inference. ICB 2009, LNCS 5558, pp. 199-208, 2009

Huang GB, Mattar M, Berg T, Learned-Miller E (2007) Labeled faces in the wild: a database for studying face recognition in unconstrained environments. University of Massachusetts, Amherst, Technical Report

Dataset of face images with different angles and head positions

Dataset contains 23,110 individuals, each contributing 28 images featuring various angles and head positions, diverse backgrounds, and attributes, along with 1 ID photo. In total, the dataset comprises over 670,000 images in formats such as JPG and PNG. It is designed to advance face recognition and facial recognition research, focusing on person re-identification and recognition systems. By utilizing this dataset… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/face-re-identification-image-dataset.

Similar face recognition has always been one of the most challenging research directions in face recognition.This project shared similar face images (SFD.zip) that we have collected so far. All images are labeld and collected from publicly available datasets such as LFW, CASIA-WebFace.We will continue to collect larger-scale data and continue to update this project.Because the data set is too large, we uploaded a compressed zip file (SFD.zip). Meanwhile here we upload a few examples for everyone to view.email: ileven@shu.edu.cn

Labeling Face Image

## Overview

Labeling Face Image is a dataset for object detection tasks - it contains Objects annotations for 964 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The BioID Face Database has been recorded and is published to give all researchers working in the area of face detection the possibility to compare the quality of their face detection algorithms with others. During the recording special emphasis has been laid on real world conditions. Therefore the testset features a large variety of illumination, background and face size. The dataset consists of 1521 gray level images with a resolution of 384x286 pixel. Each one shows the frontal view of a face of one out of 23 different test persons. For comparison reasons the set also contains manually set eye postions. The images are labeled BioID_xxxx.pgm where the characters xxxx are replaced by the index of the current image (with leading zeros). Similar to this, the files BioID_xxxx.eye contain the eye positions for the corresponding images.

Introduction

Welcome to the Caucasian Human Facial Images Dataset, curated to advance facial recognition technology and support the development of secure biometric identity systems, KYC verification processes, and AI-driven computer vision applications. This dataset is designed to serve as a robust foundation for real-world face matching and recognition use cases.

Facial Image Data

The dataset contains over 1,000 facial image sets of Caucasian individuals. Each set includes:

Diversity & Representation

Image Quality & Capture Conditions

All images were captured with real-world variability to enhance dataset robustness:

Metadata

Each participant’s data is accompanied by rich metadata to support AI model training, including:

This metadata enables targeted filtering and training across diverse scenarios.

Use Cases & Applications

This dataset is ideal for a wide range of AI and biometric applications:

Secure & Ethical Collection

Dataset Updates & Customization

To meet evolving AI demands, this dataset is regularly updated and can be customized. Available options include:

Context

All the images of faces here are generated using https://thispersondoesnotexist.com/

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2F4c3d3569f4f9c12fc898d76390f68dab%2FBeFunky-collage.jpg?generation=1662079836729388&alt=media" alt="">

Copyrighting of AI Generated images

Under US copyright law, these images are technically not subject to copyright protection. Only "original works of authorship" are considered. "To qualify as a work of 'authorship' a work must be created by a human being," according to a US Copyright Office's report [PDF].

https://www.theregister.com/2022/08/14/ai_digital_artwork_copyright/

Tagging

I manually tagged all images as best as I could and separated them between the two classes below

• Female- 3860 images
• Male- 3013 images

Some may pass either female or male, but I will leave it to you to do the reviewing. I included toddlers and babies under Male/ Female

How it works

Each of the faces are totally fake, created using an algorithm called Generative Adversarial Networks (GANs).

A generative adversarial network (GAN) is a class of machine learning frameworks designed by Ian Goodfellow and his colleagues in June 2014. Two neural networks contest with each other in a game (in the form of a zero-sum game, where one agent's gain is another agent's loss).

Given a training set, this technique learns to generate new data with the same statistics as the training set. For example, a GAN trained on photographs can generate new photographs that look at least superficially authentic to human observers, having many realistic characteristics. Though originally proposed as a form of generative model for unsupervised learning, GANs have also proved useful for semi-supervised learning, fully supervised learning,and reinforcement learning.

• https://www.youtube.com/watch?v=u8qPvzk0AfY
• https://www.youtube.com/watch?v=dCKbRCUyop8
• https://www.youtube.com/watch?v=SWoravHhsUU

Github implementation of website

• https://github.com/NVlabs/stylegan2
• https://github.com/lucidrains/stylegan2-pytorch
• https://github.com/lucidrains/lightweight-gan

How I gathered the images

Just a simple Jupyter notebook that looped and invoked the website https://thispersondoesnotexist.com/ , saving all images locally

The Pgu-Face dataset contains 896 images from 224 different subjects. All of the subjects was Iranian men and most of them live in tropical regions of the southwest of Iran. The range of age of the subject's was 16 to 82 years with average 27.89 years. In addition, we make the following information available for the subjects: age and quality of the camera in mega pixels.

A large-scale dataset for age estimation from facial images, including Indian Movie Face Database (IMFDB) with 19,906 labeled images and UTKFace with over 20,000 images labeled with age, gender, and ethnicity. Useful for AI, biometrics, and facial recognition research.

Tufts-Face-Database

Multi-modal face images (112 participants, >100,000 images in total)

7 image modalities: visible, near-infrared, thermal, computerized sketch, video, LYTRO and 3D images

Context

Tufts Face Database is the most comprehensive, large-scale (over 10,000 images, 74 females + 38 males, from more than 15 countries with an age range between 4 to 70 years old) face dataset that contains 7 image modalities: visible, near-infrared, thermal, computerized sketch, LYTRO, recorded video, and 3D images. This webpage/dataset contains the Tufts Face Database three-dimensional (3D) images. The other datasets are made available through separate links by the user.

Cross-modality face recognition is an emerging topic due to the wide-spread usage of different sensors in day-to-day life applications. The development of face recognition systems relies greatly on existing databases for evaluation and obtaining training examples for data-hungry machine learning algorithms. However, currently, there is no publicly available face database that includes more than two modalities for the same subject. In this work, we introduce the Tufts Face Database that includes images acquired in various modalities: photograph images, thermal images, near infrared images, a recorded video, a computerized facial sketch, and 3D images of each volunteer’s face. An Institutional Research Board protocol was obtained, and images were collected from students, staff, faculty, and their family members at Tufts University.

This database will be available to researchers worldwide in order to benchmark facial recognition algorithms for sketch, thermal, NIR, 3D face recognition and heterogamous face recognition.

Links to modalities of the Tufts Face Database

1. Tufts Face Database Computerized Sketches (TD_CS)

2. Tufts Face Database Thermal (TD_IR) Around+Emotion

3. Tufts Face Database Thermal Cropped (TD_IR_Cropped) Emotion only

4. Tufts Face Database Three Dimensional (3D) (TD_3D)

5. Tufts Face Database Lytro (TD_LYT) (Check Note)

6. Tufts Face Database 2D RGB Around (TD_RGB_A) (Check Note)

7. Tufts Face Database 2D RGB Emotion (TD_RGB_E) (Check Note)

8. Tufts Face Database Night Vision (NIR) (TD_NIR) (Check Note)

9. Tufts Face Database Video (TD_VIDEO) (Check Note)

10. Tufts Face Thermal2RGB Dataset

Note: Please use http instead of https. The link appears broken when https is used.

Image Acquisition

Each participant was seated in front of a blue background in close proximity to the camera. The cameras were mounted on tripods and the height of each camera was adjusted manually to correspond to the image center. The distance to the participant was strictly controlled during the acquisition process. A constant lighting condition was maintained using diffused lights.

TD_CS: Computerized facial sketches were generated using software FACES 4.0 [1], one of the most widely used software packages by law enforcement agencies, the FBI, and the US Military. The software allows researchers to choose a set of candidate facial components from the database based on their observation or memory.

TD_3D: The images were captured using a quad camera (an array of 4 cameras). Each individual was asked to look at a fixed view-point while the cameras were moved to 9 equidistant positions forming an approximate semi-circle around the individual. The 3D models were reconstructed using open-source structure-from-motion algorithms.

TD_IR_E(E stands for expression/emotion): The images were captured using a FLIR Vue Pro camera. Each participant was asked to pose with (1) a neutral expression, (2) a smile, (3) eyes closed, (4) exaggerated shocked expression, (5) sunglasses.

TD_IR_A (A stands for around): The images were captured using a FLIR Vue Pro camera. Each participant was asked to look at a fixed view-point while the cameras were moved to 9 equidistant positions forming an approximate semi-circle around the participant .

TD_RGB_E: The images were captured using a NIKON D3100 camera. Each participant was asked to pose with (1) a neutral expression, (2) a smile, (3) eyes closed, (4) exaggerated shocked expression, (5) sunglasses.

TD_RGB_A: The images were captured using a quad camera (an array of 4 visible field cameras). Each participant was asked to look at a fixed view-point while the cameras were moved to 9 equidistant positions forming an approximate semi-circle around the participant.

TD_NIR_A: The images were captured using a quad camera (an array of 4 night vision cameras). The l...

The "Labeled Faces in the Wild-a" image collection is a database of labeled, face images intended for studying Face Recognition in unconstrained images. It contains the same images available in the original Labeled Faces in the Wild data set, however, here we provide them after alignment using a commercial face alignment software. Some of our results, published in [1,2,3], were produced using these images. We show this alignment to improve the performance of face recognition algorithms. More information on how these images were aligned may be found in the two papers. We have maintained the same directory structure as in the original LFW data set, and so these images can be used as direct substitutes for those in the original image set. Note, however, that the images available here are grayscale versions of the originals. Citation: If you find these images useful and use them in your work, please follow these guidlines: Comply with any instructions specified for the original L

Face Recognition, Face Detection, Male Photo Dataset 👨

  The dataset is created on the basis of Selfies and ID Dataset

110,000+ photos of 74,000+ men from 141 countries. The dataset includes photos of people's faces. All people presented in the dataset are men. The dataset contains a variety of images capturing individuals from diverse backgrounds and age groups. Our dataset will diversify your data by adding more photos of men of different ages and ethnic groups… See the full description on the dataset page: https://huggingface.co/datasets/UniqueData/male-selfie-image-dataset.

The 110 People – Human Face Image Data is gathered through camera shot involving 110 participants, with a proper balance of gender ratio and age group distribution covering major skin tones. Each person contributes 2100 pictures with glasses/ no glasses, expressions, camera shooting angle, and lighting conditions. All Attributes are annotated such as gender, age, expression, etc. The overall accuracy rate is ≥ 97%.This dataset is suitable for face recognition, facial expression analysis, and AI training.

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

Description
SoloFace 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 Structure
The 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 Format
Each .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 Details
To improve model robustness, the following augmentation techniques were applied to the training set:

1. Geometric Transformations: Random rotation (±15 degrees), scaling (±20%), and horizontal flipping (50%).
2. Color Transformations: Brightness and contrast adjustments (±30%).
3. 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:

1. Training lightweight face detection models optimized for microcontroller deployment.
2. Benchmarking single-face detection models in resource-constrained environments.
3. Research on model robustness and efficiency.

Loading the Dataset

1. Download the dataset.
2. Extract the dataset using:

   unzip soloface-detection-dataset.zip
   

3. Dataset structure:

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

License
This 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.

Contact
For 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!

23,110 People Multi-race and Multi-pose Face Images Data. This data includes Asian race, Caucasian race, black race, brown race and Indians. Each subject were collected 29 images under different scenes and light conditions. The 29 images include 28 photos (multi light conditions, multiple poses and multiple scenes) + 1 ID photo. This data can be used for face recognition related tasks.

Introduction

Welcome to the Caucasian Human Face with Occlusion Dataset, carefully curated to support the development of robust facial recognition systems, occlusion detection models, biometric identification technologies, and KYC verification tools. This dataset provides real-world variability by including facial images with common occlusions, helping AI models perform reliably under challenging conditions.

Facial Image Data

The dataset comprises over 3,000 high-quality facial images, organized into participant-wise sets. Each set includes:

Diversity & Representation

Image Quality & Capture Conditions

To ensure robustness and real-world utility, images were captured under diverse conditions:

Metadata

Each image is paired with detailed metadata to enable advanced filtering, model tuning, and analysis:

This rich metadata helps train models that can recognize faces even when partially obscured.

Use Cases & Applications

This dataset is ideal for a wide range of real-world and research-focused applications, including:

Secure & Ethical Collection

Dataset Updates &