Face ethnicity dataset

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If this is OK with you, please click 'Accept cookies', otherwise you will see this notice on every page. For more information, please click here. VGGFace2 is a large-scale face recognition dataset.

Images are downloaded from Google Image Search and have large variations in pose, age, illumination, ethnicity and profession. VGGFace2 contains images from identities spanning a wide range of different ethnicities, accents, professions and ages. All face images are captured "in the wild", with pose and emotion variations and different lighting and occlusion conditions. Face distribution for different identities is varied, from 87 towith an average of images for each subject.

We provide loosely-cropped faces for each identity. For each image, face detection and estimated 5 keypoints are provided.

face ethnicity dataset

The copyright remains with the original owners of the image. A complete version of the license can be found here. Cookies This website uses Google Analytics to help us improve the website content. Accept cookies. Gender Distribution.

Face Size Distribution. Download We provide loosely-cropped faces for each identity. Please cite the paper if you make use of the datase. Data Loosely-cropped faces. Please register before downloading the data. Meta Information Meta information for each identity and each face image in the dataset.

Please contact the authors below if you have any queries regarding the dataset. Publications Please cite the following if you make use of the dataset.

Cao, L. Shen, W. Xie, O. Parkhi, A. VGGFace2: A dataset for recognising face across pose and age. Bibtex Abstract PDF. In this paper, we introduce a new large-scale face dataset named VGGFace2. The dataset contains 3. Images are downloaded from Google Image Search and have large variations in pose, age, illumination, ethnicity and profession e.

The dataset was collected with three goals in mind: i to have both a large number of identities and also a large number of images for each identity; ii to cover a large range of pose, age and ethnicity; and iii to minimise the label noise.

We describe how the dataset was collected, in particular the automated and manual filtering stages to ensure a high accuracy for the images of each identity. Finally, using the models trained on these datasets, we demonstrate state-of-the-art performance on the IJB-A and IJB-B face recognition benchmarks, exceeding the previous state-of-the-art by a large margin.

The dataset and models are publicly available.Residential Mobility by Race and Ethnicity reports the number and proportion of people who changed their place of residence in the past 12 months. Moved from abroad;Moved from different county, same state;Moved from different state;Moved within same county.

Entries with either no sample observations or too few sample observations available to compute either an estimate or a margin of error have been suppressed. Raw Data Explore all our datasets in raw format. Residential Mobility by Race and Ethnicity Residential Mobility by Race and Ethnicity reports the number and proportion of people who changed their place of residence in the past 12 months. Description Residential Mobility by Race and Ethnicity reports the number and proportion of people who changed their place of residence in the past 12 months.

Beginning inindividuals were presented with the option to select one or more races. In addition, the Census asked individuals to identify their race separately from identifying their Hispanic origin.

New Datasets for Disguised Face Recognition

The Census has published individual tables for the races and ethnicities provided as supplemental information to the main table that does not dissaggregate by race or ethnicity. We are not including specific combinations of two or more races as the counts of these combinations are small.

Latest Year Available Mobility Moved from abroad;Moved from different county, same state;Moved from different state;Moved within same county.

Suppression Entries with either no sample observations or too few sample observations available to compute either an estimate or a margin of error have been suppressed. Variable Residential Mobility;Margins of Error. Years in Catalog ;;;;;;;; Work Data Special Projects. About Us Team Jobs. Resources News Resources Our Code.Emotion Technology Automotive Market Research. To be precise, we have now gathered 5, face videos, for a total of 38, hours of data, representing nearly 2 billion facial frames analyzed.

This global data set is the largest of its kind — representing spontaneous emotional responses of consumers while they go about a variety of activities.

To date, the majority of our database is comprised of viewers watching media content i. In the past year, we have expanded our data repository to include other contexts such as videos of people driving their carspeople in conversational interactions and animated gifs. Transparency is really important to us at Affectiva, so we wanted to explain how we collect this data and what we do with it. Essentially, this massive data allows us to create highly accurate emotion metrics and provides us with fascinating insights into human emotional behavior.

We have now gathered 5, face videos for a total of 38, hours representing about 2 billion facial frames analyzed. Affectiva collects these face videos through our work with market research partners, such as Millward Brown, Unruly, LightspeedAdded Value, Voxpopme and LRW, as well as partners in the automotive, robotics and Human Resources space.

As a matter of fact, we have already analyzed over 4. It is important to note that every person whose face has been analyzed, has been explicitly asked to opt in to have their face recorded and their emotional expressions analyzed.

People always have the option to opt out — we recognize that emotions are private and not everyone wants their face recorded. In addition, data collection is anonymous, we never know who the individual is that the face belongs to.

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The data is representative of people engaging in an activity, such as watching content, wherever they are in the world — at their kitchen table in Bangkok or their couch in Rio de Janeiro. The face videos also represent real, spontaneous facial expressions: unfiltered and unbiased emotions in reaction to the content these folks are watching or the thing they are doing.

Also, this data captures challenging conditions, such as variations in lighting, different head movements, and variances in facial features due to ethnicity, age, gender, facial hair and glasses. There are other data sets available that are often developed in academic settings, and almost always collected in lab environments with controlled camera and lighting conditions. Frequently these academic data sets introduce bias because test subjects are often from the student body and represent a certain demographic e.

When you train and test against these posed datasets your accuracy may be high, but real world performance is poor due to the biased data and thus biased software that has been created.

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As mentioned, we have gathered this data in over 75 countries. This is important because people do not look the same around the world: there are differences in age, gender and ethnicity - and our data is representative of those demographics and cultural diversity. As we are a US-headquartered company, it can be easy to assume most of our data comes from North America or Western Europe.

That is not the case. In fact, this is the top 10 of countries we get the most videos from:. This is in contrast to more individualistic, western countries like the US, where people often amplify their emotions, especially in group settings. With this global data we can train our algorithms for this so we are uniquely able to identify nuanced and subtle emotion with high accuracy.

Our science team has built a robust infrastructure using machine learning and deep learning methodologies that allow us to train and test our algorithms at scale. So, how do you train a machine to recognize emotions, to distinguish between a smile and a smirk? You feed your learning infrastructure many examples of a smile, and many examples of a smirk. This is a smirk. We use our facial video repository to train and retrain our facial expression algorithms, also known in Machine Learning language as classifiers.

This is actually an incredible notion that our technology works as a positive feedback system — growing more intelligent every day by looking at more of its own data.

In order to find thousands of people smiling or smirking, we mine our dataset of nearly 6 million faces from all over the world. The goal of the mining exercise is to uncover more examples, and more variation of expressions from which our system can learn.

This process makes use of our ever-improving expression detectors to isolate examples where the system is uncertain. Our team of human FACS coders check these expressions and add them to a growing pool of training data.When benchmarking an algorithm it is recommendable to use a standard test data set for researchers to be able to directly compare the results. While there are many databases in use currently, the choice of an appropriate database to be used should be made based on the task given aging, expressions, lighting etc.

Another way is to choose the data set specific to the property to be tested e. Li and Anil K. Jain, ed. To the best of our knowledge this is the first available benchmark that directly assesses the accuracy of algorithms to automatically verify the compliance of face images to the ISO standard, in the attempt of semi-automating the document issuing process.

Jonathon Phillips, A. Martin, C. Wilson, M. Mansfield, J. Delac, M. Grgic, S. The FERET program set out to establish a large database of facial images that was gathered independently from the algorithm developers. Harry Wechsler at George Mason University was selected to direct the collection of this database.

The database collection was a collaborative effort between Dr. Wechsler and Dr. The images were collected in a semi-controlled environment. To maintain a degree of consistency throughout the database, the same physical setup was used in each photography session. Because the equipment had to be reassembled for each session, there was some minor variation in images collected on different dates. The database contains sets of images for a total of 14, images that includes individuals and duplicate sets of images.

A duplicate set is a second set of images of a person already in the database and was usually taken on a different day. For some individuals, over two years had elapsed between their first and last sittings, with some subjects being photographed multiple times. This time lapse was important because it enabled researchers to study, for the first time, changes in a subject's appearance that occur over a year.

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 static images in visible and infrared spectrum of 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. SCface database is freely available to research community. The paper describing the database is available here. SCfaceDB Landmarks. The database is comprised of 21 facial landmarks from face images from users annotated manually by a human operator, as described in this paper.

A close relationship exists between the advancement of face recognition algorithms and the availability of face databases varying factors that affect facial appearance in a controlled manner. The PIE database, collected at Carnegie Mellon University inhas been very influential in advancing research in face recognition across pose and illumination. Despite its success the PIE database has several shortcomings: a limited number of subjects, a single recording session and only few expressions captured.

It contains subjects, captured under 15 view points and 19 illumination conditions in four recording sessions for a total of more thanimages.

5 Million Faces — Top 15 Free Image Datasets for Facial Recognition

The Yale Face Database. Contains grayscale images in GIF format of 15 individuals.Face recognition is a common task in deep learning, and convolutional neural networks CNNs are doing a pretty good job here. I guess Facebook usually performs right at recognizing you and your friends in the uploaded images. But is this really a solved problem? What if the picture is obfuscated? What if the person impersonates somebody else? Can heavy makeup trick the neural network?

How easy is it to recognize a person who wears glasses? Disguised face recognition is still quite a challenging task for neural networks and primarily due to the lack of corresponding datasets. In this article, we are going to feature several face datasets presented recently. Each of them reflects different aspects of face obfuscation, but their goal is the same — to help developers create better models for disguised face recognition. It primarily contains images of celebrities of Indian or Caucasian origin.

The dataset focuses on a specific challenge of face recognition under the disguise covariate. This is coupled with other variations for pose, lighting, expression, background, ethnicity, age, gender, clothing, and camera quality. In total, the DFW dataset contains 1, normal face images, validation face images, 4, disguised face images, and 4, impersonator images.

The researchers extracted the images from the YouTube videos where female subjects were applying makeup to transform their appearance to resemble celebrities. It should be noted, though, that the subjects were not trying to deceive an automated face recognition system deliberately but rather, they intended to impersonate a target celebrity from a human vision perspective.

The dataset consists of makeup-transformations with two before-makeup and two after-makeup images per subject. Additionally, two face images of the target identity were taken from the Internet and included to the dataset.

However, it is important to point out that the target images are not necessarily those used by the spoofer as a reference during the makeup transformation process. The celebrities sometimes change their facial appearance drastically, and so, the researchers were trying to select target identity images that most resembled the after-makeup image. And finally, all the acquired images were subjected to face cropping.

This routine eliminates hair and accessories. The examples of the cropped images are provided below. So, in total, the MIFS dataset contains images of a subject before makeup; images of the same subject after makeup with the intention of spoofing; and images of the target subject who is being spoofed. It should also be noted that subjects are attempting to spoof multiple target identities resulting in duplicate subject identities and even multiple subjects are attempting to spoof the same target identity resulting in duplicate target identities.

It looks like glasses as a natural occlusion threaten the performance of many face detectors and facial recognition systems. The glasses are the common natural occlusion in all images of the dataset. This original set of images consists of two parts:. Another challenge is a large age gap.

Can the algorithm recognize a personality based on her picture from early childhood? Large-age gap dataset LAG was created to help developers with solving this challenging task. The dataset is constructed with photos of celebrities discovered through the Google Image Search and YouTube videos.

The large age gap may have different interpretations: from one side it refers to images with extreme difference in age e. The LAG dataset reflects both aspects of a large-age gap concept. It contains 3, images of 1, celebrities.

face ethnicity dataset

Starting from the collected images, a total of matching pairs has been generated. The face recognition problem is still topical. There are lots of challenging tasks that significantly threaten the performance of the current facial recognition systems — it turns out that even glasses are a huge problem.The first of many more face detection datasets of human faces especially created for face detection finding instead of recognition :.

Therefore, several additional feature points have been marked up, which are very useful for facial analysis and gesture recognition. This data is also available for public download here.

Many other face databases are available nowadays. The current trend is to recognize faces from different views, under varying illumination, or along time differences aging.

Here are some especially useful for testing face detection performance:. Researchers, I need your help on this Bao Face Dataset. I received it a long time ago, and now many people who used it in their work need to contact the author to get permission to use his material.

Do you know the author?

face ethnicity dataset

Can you please ask him to contact me? Thank you!!! Sooner or later, you will feel the need for an average face model when trying different locating algorithms. Here are some averaged faces:. The first of many more face detection datasets of human faces especially created for face detection finding instead of recognition : BioID Face Detection Database images with human faces, recorded under natural conditions, i.

The eye positions have been set manually and are included in the set for calculating the accuracy of a face detector. A formula is presented to normalize the decision of a match or mismatch. This is, to my knowledge, the first attempt to finally create a real test scenario with precise rules on how to calculate the accuracy of a face detector — open for all to compare their results in a scientific way!

The original article describing the database can be downloaded here. As such, it is one of the largest public face detection datasets. It consists of Eye centers of still face pictures are given! Here are some averaged faces: Average female and male face University Regensburg, Germany A typical averaged face Half female, half male about images averaged Small male average face using about images Share this page:.One of the major research areas, facial recognition has been adopted by governments and organisations for a few years now.

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Leading phone makers like Apple, Samsung, among others, have been integrating this technology into their smartphones for providing maximum security to the users. In this article, we list down 10 face datasets which can be used to start facial recognition projects. The images were crawled from Flickr and then automatically aligned and cropped.

Projects: This dataset was originally created as a benchmark for generative adversarial networks GAN. Download here.

Tufts Face Database is the most comprehensive, large-scale face dataset that contains 7 image modalities: visible, near-infrared, thermal, computerised sketch, LYTRO, recorded video, and 3D images. Size: The dataset contains over 10, images, where 74 females and 38 males from more than 15 countries with an age range between 4 to 70 years old are included.

Projects: This database will be available to researchers worldwide in order to benchmark facial recognition algorithms for sketches, thermal, NIR, 3D face recognition and heterogamous face recognition. This dataset contains expert-generated high-quality photoshopped face images where the images are composite of different faces, separated by eyes, nose, mouth, or whole face.

This dataset by Google is a large-scale facial expression dataset that consists of face image triplets along with human annotations that specify, which two faces in each triplet form the most similar pair in terms of facial expression. Projects: The dataset is intended to aid researchers working on topics related to facial expression analysis such as expression-based image retrieval, expression-based photo album summarisation, emotion classification, expression synthesis, etc.

Face Images with Marked Landmark Points is a Kaggle dataset to predict keypoint positions on face images.

Size: The size of the dataset is MP and contains facial images and up to 15 key points marked on them. Projects: This dataset can be used as a building block in several applications, such as tracking faces in images and video, analysing facial expressions, detecting dysmorphic facial signs for medical diagnosis and biometrics or facial recognition.

Labelled Faces in the Wild LFW dataset is a database of face photographs designed for studying the problem of unconstrained face recognition. Labelled Faces in the Wild is a public benchmark for face verification, also known as pair matching. Size: The size of the dataset is MB and it consists of over 13, images of faces collected from the web.

Face Detection Datasets

Projects: The dataset can be used for face verification and other forms of face recognition. UTKFace dataset is a large-scale face dataset with long age span, which ranges from 0 to years old. The images cover large variation in pose, facial expression, illumination, occlusion, resolution and other such.

Size: The dataset consists of over 20K images with annotations of age, gender and ethnicity. Projects: The dataset can be used on a variety of task such as facial detection, age estimation, age progression, age regression, landmark localisation, etc.

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This dataset is a processed version of the YouTube Faces Dataset, that basically contained short videos of celebrities that are publicly available and were downloaded from YouTube.

There are multiple videos of each celebrity up to 6 videos per celebrity. Size: The size of the dataset is 10GB, and it includes approximately videos with consecutive frames of up to frames for each original video. The overall single image frames are a total ofimages.

Projects: This dataset can be used to recognising faces in unconstrained videos. CelebFaces Attributes Dataset CelebA is a large-scale face attributes dataset with more than K celebrity images, each with 40 attribute annotations. The images in this dataset cover large pose variations and background clutter.

Size: The size of the dataset is K, which includes 10, number of identities,number of face images, and 5 landmark locations, 40 binary attributes annotations per image.

Size: The size of the dataset is 6. Projects: The dataset can be used for facial recognition, doppelganger list comparison, etc.

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