YouTube Faces DB

What is Face Recognition?

Face recognition is a biometric technology that involves identifying or verifying a person by analyzing and comparing patterns based on the person’s facial features. It leverages computer vision and machine learning techniques to detect, analyze, and recognize faces in images or videos. The technology has widespread applications, ranging from security and surveillance to user authentication and social media tagging....

List of Face Recognition Datasets

The following is a list of ten important datasets that are widely used in the field of face recognition:...

Labeled Faces in the Wild (LFW)

The Labeled Faces in the Wild (LFW) dataset is a collection of labeled human faces designed for studying the problem of unconstrained face recognition. It contains over 13,000 images of faces collected from the web, each with a label indicating the person’s name. This dataset is widely used for benchmarking face verification and recognition algorithms. It was developed by the University of Massachusetts, Amherst....

YouTube Faces DB

The YouTube Faces Database (YTF) is a dataset composed of videos of faces collected from YouTube. It contains 3,425 videos of 1,595 different individuals, providing a challenging dataset for evaluating video-based face recognition systems. Each video includes multiple frames, offering a dynamic and varied perspective of each individual’s face. This dataset was created by the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT and Tel Aviv University....

CelebA

The CelebFaces Attributes Dataset (CelebA) is a large-scale dataset with more than 200,000 celebrity images, each annotated with 40 attribute labels. It is commonly used for face attribute recognition, face detection, and landmark localization. The rich annotations make CelebA a versatile dataset for various face-related tasks. This dataset was developed by the Multimedia Laboratory at the Chinese University of Hong Kong....

CASIA WebFace

The CASIA WebFace dataset contains approximately 500,000 images of 10,575 individuals, sourced from the web. It is primarily used for face recognition research and has been instrumental in advancing deep learning techniques in this area. The large number of images and identities provides a substantial challenge for face recognition models. This dataset was created by the Chinese Academy of Sciences’ Institute of Automation....

FERET Database

The Facial Recognition Technology (FERET) Database is a widely used dataset for facial recognition research, sponsored by the US Department of Defense. It includes over 14,000 images of 1,199 individuals, captured under controlled conditions. The FERET dataset has been a benchmark for face recognition algorithms for many years. It was developed by the National Institute of Standards and Technology (NIST)....

PubFig

The Public Figures Face Database (PubFig) contains images of 200 public figures, with a total of around 58,797 images. The images are collected from the web and include a wide variety of conditions and variations. PubFig is used for evaluating face recognition systems, particularly in unconstrained environments. This dataset was created by researchers at the University of Massachusetts, Amherst....

MS-Celeb-1M

The MS-Celeb-1M dataset is a large-scale face recognition dataset with 1 million images of 100,000 celebrities. Created by Microsoft Research, it provides a massive resource for training and evaluating face recognition models. The dataset is designed to address the challenge of large-scale face recognition in diverse conditions. This dataset was developed by Microsoft Research....

VGGFace2

The VGGFace2 dataset consists of 3.31 million images of 9,131 individuals, sourced from the web. It includes a wide range of poses, ages, and lighting conditions, making it suitable for training robust face recognition models. VGGFace2 is known for its diversity and scale, contributing to advancements in face recognition research. This dataset was created by the Visual Geometry Group at the University of Oxford....

MegaFace

The MegaFace dataset is designed to evaluate the performance of face recognition algorithms at a large scale. It includes over 1 million images of 690,000 unique individuals. The primary goal of MegaFace is to test face recognition systems under large-scale and real-world conditions, pushing the limits of existing technologies. This dataset was developed by the University of Washington....

UMDFaces

The UMDFaces dataset contains over 367,000 face annotations for 8,277 subjects, collected from the web. This dataset includes both still images and video frames, providing a comprehensive resource for face detection, recognition, and landmark localization tasks. UMDFaces is notable for its detailed annotations and diverse conditions. This dataset was developed by the University of Maryland....

Conclusion

When it comes to the growth of facial recognition technology, datasets are everything. In order to train efficient and just systems for recognizing people’s faces it is important to have a well selected, vast, diverse and ethically obtained collection of data. With time as this area keeps changing focus will always be put on making use of high quality sets so as to deal with current problems while satisfying different needs brought about by more applications being invented every now and then....

Dataset for Face Recognition FAQs

How do I choose the right dataset for my face recognition project?...