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.

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 detection algorithms for more accurate recognizing faces in real-world scenarios. - Get the data

Metadata for the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2F87acb75b060abcd7838e8a9fad21fb79%2FFrame%201%20(8).png?generation=1743153407873743&alt=media" alt=""> All images come with rigorously verified metadata annotations (age, gender, ethnicity), achieving ≥95% labeling accuracy. Also images are captured under different lighting conditions and resolutions, enhancing the dataset's utility for computer vision tasks and image classifications.

💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at https://unidata.pro to discuss your requirements and pricing options.

Researchers can leverage this dataset to improve recognition technology and develop learning models that enhance the accuracy of face detections. The dataset also supports projects focused on face anti-spoofing and deep learning applications, making it an essential tool for those studying biometric security and liveness detection technologies.

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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

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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

The Face Recognition Dataset for One-Shot Learning by Globose Technology Solutions enables AI models to perform face recognition using just a single example per class. It includes diverse facial images covering various demographics, lighting conditions, and expressions for high-quality model training.

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/

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

Live Face Anti-Spoof Dataset

A live face dataset is crucial for advancing computer vision tasks such as face detection, anti-spoofing detection, and face recognition. The Live Face Anti-Spoof Dataset offered by Ainnotate is specifically designed to train algorithms for anti-spoofing purposes, ensuring that AI systems can accurately differentiate between real and fake faces in various scenarios.

Key Features:

Comprehensive Video Collection: The dataset features thousands of videos showcasing a diverse range of individuals, including males and females, with and without glasses. It also includes men with beards, mustaches, and clean-shaven faces. Lighting Conditions: Videos are captured in both indoor and outdoor environments, ensuring that the data covers a wide range of lighting conditions, making it highly applicable for real-world use. Data Collection Method: Our datasets are gathered through a community-driven approach, leveraging our extensive network of over 700k users across various Telegram apps. This method ensures that the data is not only diverse but also ethically sourced with full consent from participants, providing reliable and real-world applicable data for training AI models. Versatility: This dataset is ideal for training models in face detection, anti-spoofing, and face recognition tasks, offering robust support for these essential computer vision applications. In addition to the Live Face Anti-Spoof Dataset, FileMarket provides specialized datasets across various categories to support a wide range of AI and machine learning projects:

Object Detection Data: Perfect for training AI in image and video analysis. Machine Learning (ML) Data: Offers a broad spectrum of applications, from predictive analytics to natural language processing (NLP). Large Language Model (LLM) Data: Designed to support text generation, chatbots, and machine translation models. Deep Learning (DL) Data: Essential for developing complex neural networks and deep learning models. Biometric Data: Includes diverse datasets for facial recognition, fingerprint analysis, and other biometric applications. This live face dataset, alongside our other specialized data categories, empowers your AI projects by providing high-quality, diverse, and comprehensive datasets. Whether your focus is on anti-spoofing detection, face recognition, or other biometric and machine learning tasks, our data offerings are tailored to meet your specific needs.

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

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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

Biometric Data

FileMarket provides a comprehensive Biometric Data set, ideal for enhancing AI applications in security, identity verification, and more. In addition to Biometric Data, we offer specialized datasets across Object Detection Data, Machine Learning (ML) Data, Large Language Model (LLM) Data, and Deep Learning (DL) Data. Each dataset is meticulously crafted to support the development of cutting-edge AI models.

Data Size: 20,000 IDs

Race Distribution: The dataset encompasses individuals from diverse racial backgrounds, including Black, Caucasian, Indian, and Asian groups.

Gender Distribution: The dataset equally represents all genders, ensuring a balanced and inclusive collection.

Age Distribution: The data spans a broad age range, including young, middle-aged, and senior individuals, providing comprehensive age coverage.

Collection Environment: Data has been gathered in both indoor and outdoor environments, ensuring variety and relevance for real-world applications.

Data Diversity: This dataset includes a rich variety of face poses, racial backgrounds, age groups, lighting conditions, and scenes, making it ideal for robust biometric model training.

Device: All data has been collected using mobile phones, reflecting common real-world usage scenarios.

Data Format: The data is provided in .jpg and .png formats, ensuring compatibility with various processing tools and systems.

Accuracy: The labels for face pose, race, gender, and age are highly accurate, exceeding 95%, making this dataset reliable for training high-performance biometric models.

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.

Content

This dataset contains folders pertaining to different expressions of the human face, namely , Surprise, Anger, Happiness, Sad, Neutral, Disgust, Fear.

The folders are split into two super-folders, Training and Testing, so that it can become easier for the end user to configure any model using this data.

The training set consists of 28,079 samples in total with the testing set consisting of 7,178 samples in total. The data consists of 48x48 pixel grayscale images of faces. The faces have been automatically registered so that the face is more or less centered and occupies about the same amount of space in each image.

Acknowledgements

This dataset was obtained from the competition "Challenges in Representation Learning: Facial Expression Recognition Challenge"

This dataset was prepared by Pierre-Luc Carrier and Aaron Courville, as part of an ongoing research project. They have graciously provided the workshop organizers with a preliminary version of their dataset to use for this contest.

The code for splitting the data into different directories was provided by Jainam Mehta. Here is the link to the code: Create Training and Testing

Off-the-shelf biometric data (human face) covers 3D depth, segmentation: face organs and accessory, key points, facial expression, alpha Matte, age in variety and etc. All the Biometric Data are collected with signed authorization agreement.

This large-scale face image dataset features 10,109 individuals from various countries and ethnic backgrounds. Each subject has been captured in multiple real-world scenarios, resulting in diverse facial images under varying angles, lighting conditions, and expressions. Detailed annotations include gender, race, and age, making the dataset suitable for tasks such as facial recognition, face clustering, demographic analysis, and machine learning model training.The dataset has been validated by multiple AI companies and proven to deliver strong performance in real-world applications. All data collection, storage, and processing strictly adhere to global data protection regulations, including GDPR, CCPA, and PIPL, ensuring legal compliance and privacy preservation.

Dataset Description: Human Faces and Objects Dataset (HFO-5000) The Human Faces and Objects Dataset (HFO-5000) is a curated collection of 5,000 images, categorized into three distinct classes: male faces (1,500), female faces (1,500), and objects (2,000). This dataset is designed for machine learning and computer vision applications, including image classification, face detection, and object recognition. The dataset provides high-quality, labeled images with a structured CSV file for seamless integration into deep learning pipelines.

Column Description: The dataset is accompanied by a CSV file that contains essential metadata for each image. The CSV file includes the following columns: file_name: The name of the image file (e.g., image_001.jpg). label: The category of the image, with three possible values: "male" (for male face images) "female" (for female face images) "object" (for images of various objects) file_path: The full or relative path to the image file within the dataset directory.

Uniqueness and Key Features: 1) Balanced Distribution: The dataset maintains an even distribution of human faces (male and female) to minimize bias in classification tasks. 2) Diverse Object Selection: The object category consists of a wide variety of items, ensuring robustness in distinguishing between human and non-human entities. 3) High-Quality Images: The dataset consists of clear and well-defined images, suitable for both training and testing AI models. 4) Structured Annotations: The CSV file simplifies dataset management and integration into machine learning workflows. 5) Potential Use Cases: This dataset can be used for tasks such as gender classification, facial recognition benchmarking, human-object differentiation, and transfer learning applications.

Conclusion: The HFO-5000 dataset provides a well-structured, diverse, and high-quality set of labeled images that can be used for various computer vision tasks. Its balanced distribution of human faces and objects ensures fairness in training AI models, making it a valuable resource for researchers and developers. By offering structured metadata and a wide range of images, this dataset facilitates advancements in deep learning applications related to facial recognition and object classification.

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:

CelebA Face Recognition Triplets is a high-quality dataset designed for facial recognition research, particularly optimized for training models using triplet loss architectures. It features curated face triplets supporting robust identity verification, matching, and embedding learning.

A curated dataset of 160x160 resolution dog face images optimized for training and evaluating dog face recognition and identification models.

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 29,523 individuals. For each subject, one ID photo and 5-10 life photos were collected, the race distribution covering Asian, Caucasian, black and brown races. This data can be used for training and evaluating face recognition models, identity verification systems, and AI-based authentication solutions.

The data is used in a scoping review of face recognition using CNN architectures. The research seeks to investigate various CNN architectures and their capabilities. The data contains a list of articles that were consulted.