The VGG Face dataset is face identity recognition dataset that consists of 2,622 identities. It contains over 2.6 million images.

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 to 843, with an average of 362 images for each subject.

In this paper, we introduce a new large-scale face dataset named VGGFace2. The dataset contains 3.31 million images of 9131 subjects, with an average of 362.6 images for each subject. Images are downloaded from Google Image Search and have large variations in pose, age, illumination, ethnicity and profession (e.g. actors, athletes, politicians). 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. To assess face recognition performance using the new dataset, we train ResNet-50 (with and without Squeeze-and-Excitation blocks) Convolutional Neural Networks on VGGFace2, on MS-Celeb-1M, and on their union, and show that training on VGGFace2 lead

Dataset

This dataset was created by Ansari

Contents

Context

As name of dataset says, this dataset contains the variety of face datasets available.

Content

CFP data folder: This folder consists of around 5000 images distributed among 500 persons (10 each). source celebs folder This folder contains images 100 bollywood actors. A total of 10029 images are present. source images resolute This folder contains images of over 664 persons across the world. (Approximately size is 1.3GB ) dataset folder This folder consists low resolution images of 158 persons. crop faces folder This folder contains cropped faces of dataset folder. Cropping is done with MTCNN library.

vgg face weights h5 file Pretrained weights of VGG Facenet model. For more details visit VGG face recognition

Dataset

This dataset was created by Shiv Sharma

Contents

The VGGFace dataset contains 2,062,167 images of 2,600 subjects.

The increasingly popular application of AI runs the risk of amplifying social bias, such as classifying non-white faces as animals. Recent research has largely attributed this bias to the training data implemented. However, the underlying mechanism is poorly understood; therefore, strategies to rectify the bias are unresolved. Here, we examined a typical deep convolutional neural network (DCNN), VGG-Face, which was trained with a face dataset consisting of more white faces than black and Asian faces. The transfer learning result showed significantly better performance in identifying white faces, similar to the well-known social bias in humans, the other-race effect (ORE). To test whether the effect resulted from the imbalance of face images, we retrained the VGG-Face with a dataset containing more Asian faces, and found a reverse ORE that the newly-trained VGG-Face preferred Asian faces over white faces in identification accuracy. Additionally, when the number of Asian faces and white faces were matched in the dataset, the DCNN did not show any bias. To further examine how imbalanced image input led to the ORE, we performed a representational similarity analysis on VGG-Face's activation. We found that when the dataset contained more white faces, the representation of white faces was more distinct, indexed by smaller in-group similarity and larger representational Euclidean distance. That is, white faces were scattered more sparsely in the representational face space of the VGG-Face than the other faces. Importantly, the distinctiveness of faces was positively correlated with identification accuracy, which explained the ORE observed in the VGG-Face. In summary, our study revealed the mechanism underlying the ORE in DCNNs, which provides a novel approach to studying AI ethics. In addition, the face multidimensional representation theory discovered in humans was also applicable to DCNNs, advocating for future studies to apply more cognitive theories to understand DCNNs' behavior.

Dataset

This dataset was created by Rupak Acharya

Contents

Dataset

This dataset was created by mahmoudbelooo

Contents

This Dataset was made for experiments to algorithms to face detaction. - Yolo - VGG Face - CNN

BeyondDeepFakeDetection/VGG dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by Mohammed Hussein

Released under Apache 2.0

Contents

FAU detection results of the VGG-8 and ResNet-7 training with EmotioNet database.

VGGSound

VGG-Sound is an audio-visual correspondent dataset consisting of short clips of audio sounds, extracted from videos uploaded to YouTube.

Homepage: https://www.robots.ox.ac.uk/~vgg/data/vggsound/ Paper: https://arxiv.org/abs/2004.14368 Github: https://github.com/hche11/VGGSound

  Analysis

310+ classes: VGG-Sound contains audios spanning a large number of challenging acoustic environments and noise characteristics of real applications. 200,000+ videos: All… See the full description on the dataset page: https://huggingface.co/datasets/Loie/VGGSound.

Dataset

This dataset was created by twang_zeus

Released under MIT

Contents

Background: Williams-Beuren syndrome (WBS) is a rare genetic syndrome with a characteristic “elfin” facial gestalt. The “elfin” facial characteristics include a broad forehead, periorbital puffiness, flat nasal bridge, short upturned nose, wide mouth, thick lips, and pointed chin. Recently, deep convolutional neural networks (CNNs) have been successfully applied to facial recognition for diagnosing genetic syndromes. However, there is little research on WBS facial recognition using deep CNNs.Objective: The purpose of this study was to construct an automatic facial recognition model for WBS diagnosis based on deep CNNs.Methods: The study enrolled 104 WBS children, 91 cases with other genetic syndromes, and 145 healthy children. The photo dataset used only one frontal facial photo from each participant. Five face recognition frameworks for WBS were constructed by adopting the VGG-16, VGG-19, ResNet-18, ResNet-34, and MobileNet-V2 architectures, respectively. ImageNet transfer learning was used to avoid over-fitting. The classification performance of the facial recognition models was assessed by five-fold cross validation, and comparison with human experts was performed.Results: The five face recognition frameworks for WBS were constructed. The VGG-19 model achieved the best performance. The accuracy, precision, recall, F1 score, and area under curve (AUC) of the VGG-19 model were 92.7 ± 1.3%, 94.0 ± 5.6%, 81.7 ± 3.6%, 87.2 ± 2.0%, and 89.6 ± 1.3%, respectively. The highest accuracy, precision, recall, F1 score, and AUC of human experts were 82.1, 65.9, 85.6, 74.5, and 83.0%, respectively. The AUCs of each human expert were inferior to the AUCs of the VGG-16 (88.6 ± 3.5%), VGG-19 (89.6 ± 1.3%), ResNet-18 (83.6 ± 8.2%), and ResNet-34 (86.3 ± 4.9%) models.Conclusions: This study highlighted the possibility of using deep CNNs for diagnosing WBS in clinical practice. The facial recognition framework based on VGG-19 could play a prominent role in WBS diagnosis. Transfer learning technology can help to construct facial recognition models of genetic syndromes with small-scale datasets.

@article{DBLP:journals/corr/abs-1710-08092, author = {Qiong Cao and Li Shen and Weidi Xie and Omkar M. Parkhi and Andrew Zisserman}, title = {VGGFace2: {A} dataset for recognising faces across pose and age}, journal = {CoRR}, volume = {abs/1710.08092}, year = {2017}, url = {http://arxiv.org/abs/1710.08092}, eprinttype = {arXiv}, eprint = {1710.08092}… See the full description on the dataset page: https://huggingface.co/datasets/ProgramComputer/VGGFace2.

ZZZtong/common-accent-vgg-ready dataset hosted on Hugging Face and contributed by the HF Datasets community

PD classification results using the VGG-8 model.