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.

Includes face images of 11 subjects with 3 sets of images: one of the subject with no occlusion, one of them wearing a hat, and one of them wearing glasses. Each set consists of 5 subject positions (subject's two profile positions, one central position, and two positions angled between the profile and central positions), with 7 lighting angles for each position (completing a 180 degree arc around the subject), and 5 light settings for each angle (warm, cold, low, medium, and bright). Images are 5184 pixels tall by 3456 pixels wide and are saved in .JPG format.

Image Dataset of face images for compuer vision tasks

Dataset comprises 500,600+ images of individuals representing various races, genders, and ages, with each person having a single face image. It is designed for facial recognition and face detection research, supporting the development of advanced recognition systems. By leveraging this dataset, researchers and developers can enhance deep learning models, improve face verification and face identification techniques, and refine… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/face-recognition-image-dataset.

FGnet Markup Scheme of the BioID Face Database - The BioID Face Database is being used within the FGnet project of the European Working Group on face and gesture recognition. David Cristinacce and Kola Babalola, PhD students from the department of Imaging Science and Biomedical Engineering at the University of Manchester marked up the images from the BioID Face Database. They selected several additional feature points, which are very useful for facial analysis and gesture recognition.

Infrared Face Detection Dataset

Dataset contains 125,500+ images, including infrared images, from 4,484 individuals with or without a mask of various races, genders, and ages. It is specifically designed for research in face recognition and facial recognition technology, focusing on the unique challenges posed by thermal infrared imaging. By utilizing this dataset, researchers and developers can enhance their understanding of recognition systems and improve the recognition accuracy… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/infrared-face-recognition-dataset.

Welcome to Labeled Faces in the Wild, a database of face photographs designed for studying the problem of unconstrained face recognition. The data set contains more than 13,000 images of faces collected from the web. Each face has been labeled with the name of the person pictured. 1680 of the people pictured have two or more distinct photos in the data set. The only constraint on these faces is that they were detected by the Viola-Jones face detector.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1746215%2F92752ca2b0bbecdd3fd154b88495558d%2F1_RaupR7k7NrrTJZvop7sH-A.png?generation=1573849119616339&alt=media" alt="LFW-PEOPLE">

This dataset is a collection of JPEG pictures of famous people collected on the internet. All details are available on the official website: http://vis-www.cs.umass.edu/lfw/

Each picture is centered on a single face. Each pixel of each channel (color in RGB) is encoded by a float in range 0.0 - 1.0.

The task is called Face Recognition (or Identification): given the picture of a face, find the name of the person given a training set (gallery).

The original images are 250 x 250 pixels, but the default slice and resize arguments reduce them to 62 x 47 pixels.

Acknowledgements

We wouldn't be here without the help of others. I would like to thank Computer Vision Laboratory, university of Massachusetts for providing us with such an excellent database.

Inspiration

I had an activity in my college for facial recognition. I came up with this as the best kind of dataset for my task. I am posting it here on Kaggle to make it available for other data scientists conveniently and see what magic they can perform with this amazing dataset.

From the website

SCface is a database of static images of human faces. Images were taken in uncontrolled indoor environment using five video surveillance cameras of various qualities. Database contains 4160 static images (in visible and infrared spectrum) of 130 subjects. Images from different quality cameras mimic the real-world conditions and enable robust face recognition algorithms testing, emphasizing different law enforcement and surveillance use case scenarios.

About this dataset

The Face Recognition Dataset is a collection of 2482 annotated images of human faces collected and labeled by Noor F. Abdul Hassan, Basrah University. This Dataset was created for the purpose of training on YOLO Models.

Dataset Card for Dataset Name

This dataset card aims to be a base template for new datasets. It has been generated using this raw template.

  Dataset Details





  Dataset Description

Curated by: [More Information Needed] Funded by [optional]: [More Information Needed] Shared by [optional]: [More Information Needed] Language(s) (NLP): [More Information Needed] License: [More Information Needed]

  Dataset Sources [optional]

Repository: [More… See the full description on the dataset page: https://huggingface.co/datasets/silk-road/IMDB-Face-Recognition.

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

Black people Face Detection Dataset: 3M+ Identities

Large human faces dataset for face recognition models (10M+ images)

  Share with us your feedback and recieve additional samples for free!😊





  Full version of dataset is availible for commercial usage - leave a request on our website Axon Labs to purchase the dataset 💰

Dataset targeting 1:N and 1:1 NIST face recognition tests. Dataset contains 3M individuals, each with 3-5 images containing their faces The… See the full description on the dataset page: https://huggingface.co/datasets/AxonData/Black_People_Face_Recognition.

Data Collection - TagX can provides the dataset based on following scenarios to train a biasfree face analysis and detection dataset- Single and multiple faces images Monk skin-tones covered All Facial angles included

Metadata for Face Images- We can provide following metadata along with the collected images Age Range Distance from camera Emotion State Accessories present(Eyeglasses, hat etc.) pose with the values of pitch, roll, and yaw.

Annotation of Face Images- We can provides annotation for face detection applications like Bounding box annotation, Landmark annotation or polygon annotation. We have a dataset prepared with bounding box annotation around faces for 30000 images.

Elderly Face Detection

## Overview

Elderly Face Detection is a dataset for object detection tasks - it contains Face Detection annotations for 735 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).

Unidata’s Infrared Face Recognition dataset for improving security systems and enhancing AI performance in low-light condition

Eye Position File Format - The eye position files are text files containing a single comment line followed by the x and the y coordinate of the left eye and the x and the y coordinate of the right eye separated by spaces. Note that we refer to the left eye as the person's left eye. Therefore, when captured by a camera, the position of the left eye is on the image's right and vice versa.

Face Recognition 2.1

## Overview

Face Recognition 2.1 is a dataset for classification tasks - it contains Los5 annotations for 1,709 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 [MIT license](https://creativecommons.org/licenses/MIT).

Introduction

The Middle Eastern Children Facial Image Dataset is a thoughtfully curated collection designed to support the development of advanced facial recognition systems, biometric identity verification, age estimation tools, and child-specific AI models. This dataset enables researchers and developers to build highly accurate, inclusive, and ethically sourced AI solutions for real-world applications.

Facial Image Data

The dataset includes over 1000 high-resolution image sets of children under the age of 18. Each participant contributes approximately 15 unique facial images, captured to reflect natural variations in appearance and context.

Diversity and Representation

Quality and Image Conditions

To ensure robust model training and generalizability, images are captured under varied natural conditions:

Metadata

Each child’s image set is paired with detailed, structured metadata, enabling granular control and filtering during model training:

This metadata is essential for applications that require demographic awareness, such as region-specific facial recognition or bias mitigation in AI models.

Applications

This dataset is ideal for a wide range of computer vision use cases, including:

Ethical Collection and Data Security

We maintain the highest ethical and security standards throughout the data lifecycle:

Night Vision Face Recognition

## Overview

Night Vision Face Recognition is a dataset for object detection tasks - it contains Face annotations for 2,768 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).

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...

Introduction

Welcome to the Middle Eastern 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