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. Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4. For details, see the Google Developers Site Policies. Install Learn Introduction. TensorFlow Lite for mobile and embedded devices. TensorFlow Extended for end-to-end ML components.

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Overview Audio. Image classification. Object detection.Last Updated on November 22, Face recognition is a computer vision task of identifying and verifying a person based on a photograph of their face. Recently, deep learning convolutional neural networks have surpassed classical methods and are achieving state-of-the-art results on standard face recognition datasets.

Although the model can be challenging to implement and resource intensive to train, it can be easily used in standard deep learning libraries such as Keras through the use of freely available pre-trained models and third-party open source libraries. In this tutorial, you will discover how to develop face recognition systems for face identification and verification using the VGGFace2 deep learning model. Discover how to build models for photo classification, object detection, face recognition, and more in my new computer vision bookwith 30 step-by-step tutorials and full source code.

Face recognition is the general task of identifying and verifying people from photographs of their face. A face recognition system is expected to identify faces present in images and videos automatically. It can operate in either or both of two modes: 1 face verification or authenticationand 2 face identification or recognition.

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A contribution of the paper was a description of how to develop a very large training dataset, required to train modern-convolutional-neural-network-based face recognition systems, to compete with the large datasets used to train models at Facebook and Google. To this end we propose a method for collecting face data using knowledge sources available on the web Section 3. We employ this procedure to build a dataset with over two million faces, and will make this freely available to the research community.

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This dataset is then used as the basis for developing deep CNNs for face recognition tasks such as face identification and verification. Specifically, models are trained on the very large dataset, then evaluated on benchmark face recognition datasets, demonstrating that the model is effective at generating generalized features from faces. They describe the process of training a face classifier first that uses a softmax activation function in the output layer to classify faces as people.

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This layer is then removed so that the output of the network is a vector feature representation of the face, called a face embedding. The model is then further trained, via fine-tuning, in order that the Euclidean distance between vectors generated for the same identity are made smaller and the vectors generated for different identities is made larger.

This is achieved using a triplet loss function. Triplet-loss training aims at learning score vectors that perform well in the final application, i.

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A deep convolutional neural network architecture is used in the VGG stylewith blocks of convolutional layers with small kernels and ReLU activations followed by max pooling layers, and the use of fully connected layers in the classifier end of the network. Qiong Cao, et al.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 whole dataset is split to a training set including identites and a test set including identites. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ODNI, IARPA, or the U.

The U. Government is authorized to reproduce and distribute reprints for Governmental purpose notwithstanding any copyright annotation thereon. 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. MD5 MD5. Cao, L. Shen, W. Xie, O.

Parkhi, A.Abstract : This data consists of black and white face images of people taken with varying pose straight, left, right, upexpression neutral, happy, sad, angryeyes wearing sunglasses or notand size.

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Each image can be characterized by the pose, expression, eyes, and size. There are 32 images for each person capturing every combination of features.

To view the images, you can use the program xv. This directory contains 20 subdirectories, one for each person, named by userid. Each of these directories contains several different face images of the same person. You will be interested in the images with the following naming convention:. If you've been looking closely in the image directories, you may notice that some images have a. As it turns out, 16 of the images taken have glitches due to problems with the camera setup; these are the.

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More information and C code for loading the images is available here: [Web Link]. Xiaofeng He and Partha Niyogi. Locality Preserving Projections. Marina Meila and Michael I. Learning with Mixtures of Trees. Journal of Machine Learning Research, 1.

You may use this material free of charge for any educational purpose, provided attribution is given in any lectures or publications that make use of this material.

vggface2 dataset download

Center for Machine Learning and Intelligent Systems. CMU Face Images Data Set Download : Data FolderData Set Description Abstract : This data consists of black and white face images of people taken with varying pose straight, left, right, upexpression neutral, happy, sad, angryeyes wearing sunglasses or notand size.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. 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.

VGGFace2: A dataset for recognising faces across pose and age

The VGGFace2 dataset contains 3. The Images were downloaded from Google Image Search and show large variations in pose, age, lighting and background.

vggface2 dataset download

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. 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.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.

vggface2 dataset download

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.

vggface2 dataset download

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.Released: Mar 12, View statistics for this project via Libraries. Pytorch model weights were initialized using parameters ported from David Sandberg's tensorflow facenet repo. Also included in this repo is an efficient pytorch implementation of MTCNN for face detection prior to inference. These models are also pretrained. Both pretrained models were trained on x px images, so will perform best if applied to images resized to this shape.

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By default, the above models will return dimensional embeddings of images. Face recognition can be easily applied to raw images by first detecting faces using MTCNN before calculating embedding or probabilities using an Inception Resnet model. In most situations, the best way to implement face recognition is to use the pretrained models directly, with either a clustering algorithm or a simple distance metrics to determine the identity of a face.

However, if finetuning is required i. To use this code in your own git repo, I recommend first adding this repo as a submodule. Note that the dash '-' in the repo name should be removed when cloning as a submodule as it will break python when importing:. The equivalence of the outputs from the original tensorflow models and the pytorch-ported models have been tested and are identical:.

Schroff, D. Kalenichenko, J. Cao, L. Shen, W. Xie, O. Parkhi, A. Yi, Z. Lei, S. Liao and S. Zhang, Z. Li and Y. Mar 12, Mar 3, Feb 17, Feb 15, Feb 9, Jan 7, Jan 6, Dec 29, Dec 15, Nov 13, Nov 2, Sep 19, Sep 11, Sep 10,


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