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.

Face Detection - Object Detection & Face Recognition Dataset

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

The dataset is a collection of images (selfies) of people and bounding box labeling for their faces. It has been specifically curated for face detection and face recognition tasks. The dataset encompasses diverse demographics, age, ethnicities, and genders.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F01348572e2ae2836f10bc2f2da381009%2FFrame%2050%20(1).png?generation=1699439342545305&alt=media" alt="">

The dataset is a valuable resource for researchers, developers, and organizations working on age prediction and face recognition to train, evaluate, and fine-tune AI models for real-world applications. It can be applied in various domains like psychology, market research, and personalized advertising.

👉 Legally sourced datasets and carefully structured for AI training and model development. Explore samples from our dataset of 95,000+ human images & videos - Full dataset

Metadata for the full dataset:

• assignment_id - unique identifier of the media file
• worker_id - unique identifier of the person
• age - age of the person
• true_gender - gender of the person
• country - country of the person
• ethnicity - ethnicity of the person
• photo_1_extension, photo_2_extension, …, photo_15_extension - photo extensions in the dataset
• photo_1_resolution, photo_2_resolution, …, photo_15_resolution - photo resolution in the dataset

OTHER BIOMETRIC DATASETS:

• Anti Spoofing Real Dataset
• Antispoofing Replay Dataset
• Selfies, ID Images dataset (5591 sets of 15 files)
• Selfies and video dataset (4 052 sets)
• Dataset of bald people, 5000 images

🧩 This is just an example of the data. Leave a request here to learn more

Dataset structure

• images - contains of original images of people
• labels - includes visualized labeling for the original images
• annotations.xml - contains coordinates of the bbox, created for the original photo

Data Format

Each image from images folder is accompanied by an XML-annotation in the annotations.xml file indicating the coordinates of the polygons and labels . For each point, the x and y coordinates are provided.

Example of XML file structure

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F19e61b2d0780e9db80afe4a0ce879c4b%2Fcarbon.png?generation=1699440100527867&alt=media" alt="">

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keywords: biometric system, biometric system attacks, biometric dataset, face recognition database, face recognition dataset, face detection dataset, facial analysis, object detection dataset, deep learning datasets, computer vision datset, human images dataset, human faces dataset

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.

Gender Detection & Classification - face recognition dataset

The dataset is created on the basis of Medical Masks Dataset dataset

Dataset Description:

The dataset comprises a collection of photos of people, organized into folders labeled "women" and "men." Each folder contains a significant number of images to facilitate training and testing of gender detection algorithms or models.

The dataset contains a variety of images capturing female and male individuals from diverse backgrounds, age groups, and ethnicities.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F1c4708f0b856f7889e3c0eea434fe8e2%2FFrame%2045%20(1).png?generation=1698764294000412&alt=media" alt="">

This labeled dataset can be utilized as training data for machine learning models, computer vision applications, and gender detection algorithms.

👉 Legally sourced datasets and carefully structured for AI training and model development. Explore samples from our dataset of 95,000+ human images & videos - Full dataset

Metadata for the full dataset:

• assignment_id - unique identifier of the media file
• worker_id - unique identifier of the person
• age - age of the person
• true_gender - gender of the person
• country - country of the person
• ethnicity - ethnicity of the person
• photo_1_extension, photo_2_extension, photo_3_extension, photo_4_extension - photo extensions in the dataset
• photo_1_resolution, photo_2_resolution, photo_3_extension, photo_4_resolution - photo resolution in the dataset

🧩 This is just an example of the data. Leave a request here to learn more

Content

The dataset is split into train and test folders, each folder includes: - folders women and men - folders with images of people with the corresponding gender, - .csv file - contains information about the images and people in the dataset

File with the extension .csv

• file: link to access the file,
• gender: gender of a person in the photo (woman/man),
• split: classification on train and test

🚀 You can learn more about our high-quality unique datasets here

keywords: biometric system, biometric system attacks, biometric dataset, face recognition database, face recognition dataset, face detection dataset, facial analysis, gender detection, supervised learning dataset, gender classification dataset, gender recognition 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.

The Tufts Face Database is a comprehensive collection of human face images, ideal for facial recognition, biometric verification, and computer vision model training. It includes diverse data by ethnicity, age, gender, and region for robust AI development.

This Dataset is created by organizing the WIDER FACE dataset. WIDER FACE dataset is a face detection benchmark dataset, of which images are selected from the publicly available WIDER dataset. We chose 32,203 images and labeled 393,703 faces with a high degree of variability in scale, pose, and occlusion as depicted in the sample images. WIDER FACE dataset is organized based on 61 event classes. For each event class, we randomly select 40%/10%/50% of data as training, validation, and testing sets. We adopt the same evaluation metric employed in the PASCAL VOC dataset.

Original Dataset http://shuoyang1213.me/WIDERFACE/

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.

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 Detection and Re-identification, Faces Dataset

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

This dataset comprises a collection of 6 photos of 50 people, split into two folders: "train" and "test". The "train" folder contains 5 images, while the "test" folder contains 1 image to evaluate the trained model's performance.

The dataset contains a variety of images capturing individuals from diverse backgrounds, age groups, and ethnicities.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fe4d48c1613eb5e72c76dc7745cd4f229%2FFrame%2046.png?generation=1698861001765019&alt=media" alt="">

The dataset can be utilized for a wide range of tasks, including face recognition, emotion detection, age estimation, gender classification, or any problem related to human image analysis.

👉 Legally sourced datasets and carefully structured for AI training and model development. Explore samples from our dataset of 95,000+ human images & videos - Full dataset

Metadata for the full dataset:

• assignment_id - unique identifier of the media file
• worker_id - unique identifier of the person
• age - age of the person
• true_gender - gender of the person
• country - country of the person
• ethnicity - ethnicity of the person
• photo_1_extension, photo_2_extension, …, photo_15_extension - photo extensions in the dataset
• photo_1_resolution, photo_2_resolution, …, photo_15_resolution - photo resolution in the dataset

OTHER BIOMETRIC DATASETS:

• Anti Spoofing Real Dataset
• Antispoofing Replay Dataset
• Selfies, ID Images dataset (5591 sets of 15 files)
• Selfies and video dataset (4 052 sets)
• Dataset of bald people, 5000 images

🧩 This is just an example of the data. Leave a request here to learn more

Content

The dataset is split into train and test folders, each folder includes: - train - contains folders 0, 1, ..., 49 with 5 images of each person in the dataset, - test - contains image of each person in the dataset corresponding to the number of the subfolder in the train folder, - .csv file - contains information about the images and people in the dataset

File with the extension .csv

• person: id of the person,
• file_1: link to access the first photo,
• file_2: link to access the second photo,
• file_3: link to access the third photo,
• file_4: link to access the fourth photo,
• file_5: link to access the fifth photo,
• test: link to access the test photo

🚀 You can learn more about our high-quality unique datasets here

keywords: biometric system, biometric system attacks, biometric dataset, face recognition database, face recognition dataset, face detection dataset, facial analysis, supervised learning dataset, person re-identification, person re-identification dataset, person re-ID dataset

Facial recognition datasets consist solely of images of faces, with no additional annotations. They include diverse examples of facial features, poses, and lighting conditions, and are used to train and evaluate facial recognition systems for tasks like face detection and recognition.

Description The collecting scenes of this dataset include indoor scenes and outdoor scenes. The data includes male and female. The age distribution ranges from child to the elderly, the young people and the middle aged are the majorities. The collecting device is realsense D453i. The data diversity includes multiple age periods, multiple facial postures, multiple scenes. The data can be used for tasks such as infrared face recognition. For more details, please visit: https://www.nexdata.ai/datasets/computervision/1134?source=Kaggle

Specifications Data size 5,993 people, 28 images for each person (RGB + IR) Population distribution race distribution: Asian; gender distribution: 3,074 male, 2,919 female; age distribution:ranging from teenager to the elderly, the middle-aged and young people are the majorities Collecting environment indoor scenes, outdoor scenes Data diversity multiple age periods, multiple facial postures, multiple scenes Device Realsense D453i, the resolution is 1,280*720 Data format the image data format is .jpg, the camera parameter information file format is .txt Annotation content label the person – ID, race, gender, age, facial action, collecting scene Accuracy rate based on the accuracy of the actions, the accuracy exceeds 97%; the accuracy of label annotation is not less than 97%

Get the Dataset This is just an example of the data. To access more sample data or request the price, contact us at info@nexdata.ai

Face Recognition Train

Face recognition is a technology that involves identifying or verifying individuals by analyzing their facial features. It has gained significant popularity and has various applications, including security systems, access control, surveillance, and personalized user experiences.

The process of face recognition typically involves the following steps:

Face detection: A face detection algorithm is used to locate and extract faces from an image or a video frame. This step helps in isolating the facial region for further analysis.

Face alignment and preprocessing: The extracted face images are usually aligned to a standardized size and orientation to account for variations in pose, scale, and rotation. Preprocessing techniques may be applied to normalize lighting conditions, remove noise, and enhance the quality of the images.

Feature extraction: Meaningful features are extracted from the aligned face images to represent the unique characteristics of each individual. These features are often represented as numerical vectors, capturing specific facial attributes or patterns. Traditional methods like Eigenfaces, Fisherfaces, or Local Binary Patterns (LBP) can be used, but deep learning-based approaches like Convolutional Neural Networks (CNNs) have shown superior performance in recent years.

Feature encoding and representation: The extracted features are encoded into a compact representation, making it easier to compare and match them against other faces. Techniques like Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), or more advanced methods like Siamese networks or Triplet Loss can be employed for encoding the face features.

Face matching and recognition: During this stage, the extracted and encoded features are compared to a database of known faces or a set of reference features. The goal is to find the closest match or determine the identity of the individual represented by the face image. Various similarity metrics such as Euclidean distance, cosine similarity, or more sophisticated techniques like metric learning can be utilized for face matching.

Decision and classification: Based on the comparison results, a decision is made to recognize or classify the input face image. If a match is found within the database, the system can provide the identity of the person associated with the recognized face.

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

This dataset contains 11,113 people with gauze masks, each contributing 7 images, for a total of 77,791 images. The dataset covers multiple mask types, ages, races, light conditions and scenes. This data can be applied to computer vision tasks such as occluded face detection and recognition, masked face recognition and security systems.

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 dataset is “cleaned” and has… See the full description on the dataset page: https://huggingface.co/datasets/AxonData/Black_People_Face_Recognition.

A dataset of 118 individuals with a variety of facial expressions and corresponding depth profiles.

1. Specifications Product : Biometric Data

Data size : 200,000 ID

Race distribution : black people, Caucasian people, brown(Mexican) people, Indian people and Asian people

Gender distribution : gender balance

Age distribution : young, midlife and senior

Collecting environment : including indoor and outdoor scenes

Data diversity : different face poses, races, ages, light conditions and scenes Device : cellphone

Data format : .jpg/png

Accuracy : the accuracy of labels of face pose, race, gender and age are more than 97%

1. About Nexdata Nexdata owns off-the-shelf PB-level Large Language Model(LLM) Data, 3 million hours of Speech Data and 800TB of Imagery Data. These ready-to-go Machine Learning(ML) Data support instant delivery, quickly improve the accuracy of AI models. For more details, please visit us at https://www.nexdata.ai/datasets/computervision?source=Datarade

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.

The Face datasets I downloaded from kaggle: https://www.kaggle.com/datasets/lucifierx/face-shape-classification