Vggface2 dataset

The dataset contains 3. Images are downloaded from Google Image Search and have large variations in pose, age, illumination, ethnicity and profession e. The whole dataset is split to a training set including identites dsp basics a test set including identites.

Higher is better. More evaluation results can be found in the paper. ResNet models follow the architectural configuration in [3] and SE-ResNet models follow the one in [4]. Caffe : SE models use the "Axpy" layer which is a combination of two consecutive operations channel-wise scale and element-wise summation More information can be found here.

Please note that the input mean vector is in BGR order as opencv is used for loading images. This bounding box is then extended by a factor 0. The coordinates of bounding boxes and 5 facial keypoints referring to the loosely cropped faces can be found here. Whitelam, E. Taborsky, A. Blanton, B. Maze, J. Adams, T. Miller, N. Kalka, A. Jain, J. Duncan, K. Allen, J. Cheney and P. Guo, L. Zhang, Y.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.

vggface2 dataset

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Training using the VGGFace2 dataset

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Email Address. Sign In. Access provided by: anon Sign Out. 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.

Article :. DOI: Need Help?We introduce a new large-scale face dataset named VGGFace2.

VGGFace2 Dataset for Face Recognition

The dataset contains 3. Images are downloaded from Google Image Search and have large variations in pose, age, illumination, ethnicity and profession e. The whole dataset is split to a training set including identites and a test set including identites.

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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 minimize the label noise through automated and manual filtering, The whole dataset is split to a training set including identites and a test set including identites. Loosely cropped faces for training. Loosely cropped faces for testing.

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Mut1ny's Face processor Demonstration on the LFW dataset

Cao, L. Shen, W. Xie, O. Parkhi, A.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 minimize 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. Datasets and models are publicly available. Qiong Cao. Li Shen.

VGGFace2: A dataset for recognising faces across pose and age

Weidi Xie. Omkar M. Andrew Zisserman. Deep convolutional neural networks CNNs have greatly improved the Face Recent progress in face detection including keypoint detectionand re Web-scraped, in-the-wild datasets have become the norm in face recogniti Recent face recognition experiments on a major benchmark LFW show stunni Increased interest of scientists, producers and consumers in sheep ident Recent advances in deep learning have significantly increased the perfor Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.

Concurrent with the rapid development of deep Convolutional Neural Networks CNNsthere has been much recent effort in collecting large scale datasets to feed these data-hungry models. The former focuses on depth many images of one subject and the latter on breadth many subjects with limited images per subject.

However, none of these datasets was specifically designed to explore pose and age variation. We address that here by designing a dataset generation pipeline to explicitly collect images with a wide range of pose, age, illumination and ethnicity variations of human faces. We make the following four contributions: first, we have collected a new large scale dataset, VGGFace2, for public release. The curated version, where label noise is removed by human annotators, hasimages with approximately images per identity.

It contains 4. However, an average of only 7 images per identity makes it restricted in its per identity face variation. This is a very useful dataset, and we employ it for pre-training in this paper. However, it has two limitations: i while it has the largest number of training images, the intra-identity variation is somewhat restricted due to an average of 81 images per person; ii images in the training set were directly retrieved from a search engine without manual filtering, and consequently there is label noise.

Apart form these public datasets, Facebook and Google have large in-house datasets. The VGGFace2 dataset contains 3. The Images were downloaded from Google Image Search and show large variations in pose, age, lighting and background. The dataset is approximately gender-balanced, with In addition, pose yaw, pitch and roll and apparent age information are estimated by our pre-trained pose and age classifiers Pose, age statistics and example images are shown in Figure. The dataset is divided into two splits: one for training having classes, and one for evaluation test with classes.This website uses Google Analytics to help us improve the website content.

This requires the use of standard Google Analytics cookies, as well as a cookie to record your response to this confirmation request. 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. 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.

vggface2 dataset

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.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 minimize 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.

Concurrent with the rapid development of deep Convolutional Neural Networks, there has been much recent effort in collecting large scale datasets to feed these data-hungry models. The former focuses on depth many images of one subject and the latter on breadth many subjects with limited images per subject.

However, none of these datasets was specifically designed to explore pose and age variation. We address that here by designing a dataset generation pipeline to explicitly collect images with a wide range of pose, age, illumination and ethnicity variations of human faces. We make the following four contributions: first, we have collected a new large scale dataset, VGGFace2, for public release.

This dataset had 5identities with 13images. The curated version, where label noise is removed by human annotators, hasimages with approximately images per identity. It contains 4. However, an average of only 7 images per identity makes it restricted in its per identity face variation. This is a very useful dataset, and we employ it for pre-training in this paper. However, it has two limitations: i while it has the largest number of training images, the intra-identity variation is somewhat restricted because of an average of 81 images per person; ii images in the training set were directly retrieved from a search engine without manual filtering, and consequently there is label noise.

Apart form these public datasets, Facebook and Google have large in-house datasets. Compared with the public datasets, VGGFace2 is advantageous in two ways: first, the images have large pose, age, ethnicity variations by design; second, with identities and an average of images per subject, the dataset guarantees both inter- and intra-class variations, while annotation noise is minimized through manual filtering.

The VGGFace2 dataset contains 3. The Images were downloaded from Google Image Search and show large variations in pose, age, lighting and background. The dataset is approximately gender-balanced, with In addition, pose yaw, pitch and roll and apparent age information are estimated by our pre-trained pose and age classifiers.

The dataset is divided into two splits: one for training having classes, and one for evaluation with classes. The VGGFace2 provides annotation to enable evaluation on two scenarios: face matching across different poses, and face matching across different ages. Pose templates. A template here consists of five faces from the same subject with a consistent pose.

This pose can be frontal, three-quarter or profile view. For a subset of subjects of the evaluation set, two templates 5 images per template are provided for each pose view.

vggface2 dataset

Consequently there are 1.


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