Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments

By Gary B. Huang, Marwan A. Mattar, Tamara L. Berg, Eric Learned-Miller. 2008

6,937 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Evaluation & Benchmarks

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Citations in the corpus

ImageNet Large Scale Visual Recognition Challenge (2014, methodology): “d 3.3.3. Standardized challenges. There are several datasets with standardized online evaluation similar to ILSVRC: the aforementioned PASCAL VOC (Everingham et al., 2012), Labeled Faces in the Wild (Huang et al., 2007) for unconstrained face recognition, Reconstruction meets Recognition (Urtasun et”
Microsoft COCO: Common Objects in Context (2014, background): “ly represented by a bounding box, Figure1(b). Early algorithms focused on face detection [27] using various ad hoc datasets. Later, more realistic and challenging face detection datasets were created [28]. Another popular challenge is the detection of pedestrians for which several datasets have been”
Datasheets for datasets (2018, methodology): “Example datasheet for Labeled Faces in the Wild [14], page 4.”
FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age (2019, background): “†LFWA+ [37] LFW [23] (Newspapers) 13K X X merged merged X X no”
FaceNet: A unified embedding for face recognition and clustering (2015, background): “Labeled Faces in the Wild (LFW) is the de-facto academic test set for face verification [7].”
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks (2015, methodology): “We depict additional samples from an unsupervised CatGAN model trained on MNIST and Labeled Faces in the Wild (LFW)(Huang et al., 2007) in Figures 7 and 8”
Deep Learning Face Attributes in the Wild (2014, methodology): “It achieves state-of-the-art attribute classification results on both the challenging CelebFaces [26] and LFW [12] datasets, improving existing methods by 8 and 13 percent, respectively.”
Building high-level features using large scale unsupervised learning (2011, methodology): “ However, for better parallelism and better features, we used the version without tied weights. 4 4 Test set The test set consists of 37000 images sampled from two datasets: Labeled Faces In the Wild [10] and ImageNet [4]. There are 13026 faces sampled from non-aligned Labeled Faces in The Wild.6 Th”
Autoencoding beyond pixels using a learned similarity metric (2015, methodology): “To imitate the original experiment, we use Labeled faces in the wild (LFW) images (Huang et al., 2007) with attributes (Kumar et al.”
The Non-IID Data Quagmire of Decentralized Machine Learning (2019, methodology): “We use veriﬁcation accuracy on the LFW dataset (Huang et al., 2007) as the model quality metric.”
SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems (2018, methodology): “The benign images dataset contains 400 images randomly selected from the LFW dataset [27].”
Exploiting Unintended Feature Leakage in Collaborative Learning (2018, methodology): “For example, when the model is trained on the LFW dataset [28] to recognize gender or race, we infer”
Machine Learning Models that Remember Too Much (2017, methodology): “egory) and 10,000 test images [40]. Each image has 32x32 pixels, each pixel has 3 values corresponding to RGB intensities. Labeled Faces in the Wild (LFW) contains 13,233 images for 5,749 individuals [33, 45]. We use 75% for training, 25% for testing. For the gender classification task, we use addit”
Defeating Image Obfuscation with Deep Learning (2016, methodology): “Zhou et al. [56] and Parkhi et al. [36] presented CNNs for the Labeled Faces in the Wild database [17] supplemented with large datasets of Web images.”
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) (2017, background): “For the case of fairness analysis (e.g., gender, protected groups), curated examples are readily available (Huang et al., 2007).”
Faceless Person Recognition: Privacy Implications in Social Media (2016, background): “Most previous work on person recognition in images has focused either on face images [10] (mainly frontal head) or on the surveillance scenario [11, 12], where the full body is visible, usually in low resolution.”