MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition

By Yandong Guo, Lei Zhang, Yuxiao Hu, Xiaodong He, Jianfeng Gao. 2016

2,204 citations recorded by the source corpus.

In this paper, we design a benchmark task and provide the associated datasets for recognizing face images and link them to corresponding entity keys in a knowledge base. More specifically, we propose a benchmark task to recognize one million celebrities from their face images, by using all the possibly collected face images of this individual on the web as training data. The rich information provided by the knowledge base helps to conduct disambiguation and improve the recognition accuracy, and contributes to various real-world applications, such as image captioning and news video analysis. Associated with this task, we design and provide concrete measurement set, evaluation protocol, as well as training data. We also present in details our experiment setup and report promising baseline results. Our benchmark task could lead to one of the largest classification problems in computer vision. To the best of our knowledge, our training dataset, which contains 10M images in version 1, is the largest publicly available one in the world.

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age (2019, background): “To date, numerous large scale face image datasets [22, 31, 13, 69, 37, 24, 41, 68, 15, 27, 46, 8, 39] have been proposed and fostered research and development for automated face detection [35, 21], alignment [66, 44], recognition [57, 49], generation [67, 5, 26, 58], modiﬁcation [3, 32, 19], and…”
This Face Does Not Exist... But It Might Be Yours! Identity Leakage in Generative Models (2020, neutral): “3M) 512 Euclidean Distance ArcFace [7] ResNet-100 MS-Celeb-1M [12] (∼10M) 512 Cosine Distance SphereFace [19] ResNet-64 CASIA-Webface [30] (∼0.”
Diversity in Faces (2019, methodology): “7 Coding Scheme 7: Age Prediction Figure 13(a) summarizes the feature distribution for age prediction in coding scheme 7, where we bin the age values into seven groups: [0-3],[4-12],[13-19],[20-30],[31-45],[46-60],[61-].”
Large Scale Incremental Learning (2019, methodology): “MS-Celeb-1M-base is a smaller yet nearly noise-free version of MS-Celeb-1M [6], which has near 100,000 classes with a total of 1.”
VoxCeleb2: Deep Speaker Recognition (2018, background): “Such a mapping has been learnt effectively for face images, through the use of deep CNN architectures [1, 2, 3] trained on large-scale face datasets [4, 5, 6].”
Incremental Classifier Learning with Generative Adversarial Networks (2018, methodology): “2 million aligned face images, which is a smaller yet nearly noise-free version of MS-Celeb-1M [6] dataset.”
VGGFace2: A Dataset for Recognising Faces across Pose and Age (2017, methodology): “The following experiments are developed under four settings: (a) networks are learned from scratch on VGGFace [16] (VF for short); (b) networks are learned from scratch on MS-Celeb-1M (MS1M for short) [7]; (c) networks are learned from scratch on VGGFace2 (VF2 for short); and, (d) networks are first”
Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations (2017, methodology): “Next, we evaluate the ML-VAE on the face aligned version of the MSCeleb-1M dataset Guo et al. [2016]. The dataset was constructed by retrieving approximately 100 images per celebrity from popular search engines, and noise has not been removed from the dataset.”
Large-Scale Long-Tailed Recognition in an Open World (2019, methodology): “MS1M-LT: To create a long-tailed version of the MS1MArcFace dataset [14, 8], we sample images for each identity with a probability proportional to the image numbers of each identity.”
The iNaturalist Challenge 2017 Dataset (2017, neutral): “[11, 27, 8].”
Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis (2017, methodology): “The feature extraction network, Light CNN, is trained on MS-Celeb-1M [11] and ﬁne-tuned on the original images of MultiPIE.”
Continual Representation Learning for Biometric Identification (2020, background): “The recent improvements in biometric identiﬁcation are achieved by learning better representation with different losses, e.g. , softmax-based losses [11, 34, 67], triplet-based losses [9,20,52] and other kinds of losses [8,60], on large-scale image datasets [17,22,28,30,66,76,78].”
Multimodal Intelligence: Representation Learning, Information Fusion, and Applications (2019, methodology): “For example, based on a database of celebrities [192], a the CaptionBot application was developed to describe the components (such as activities) of”
The Devil of Face Recognition is in the Noise (2018, methodology): “We investigate further the noise distribution of the two largest public datasets to date, MS-Celeb-1M [5] and MegaFace [13].”
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss (2019, background): “Modern real-world large-scale datasets often have long-tailed label distributions [51, 28, 34, 12, 15, 50, 40].”
FaceID-GAN: Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis (2018, methodology): “To compute these scores, we employ a face recognition model [27] trained on MS-Celeb-1M [11], which is totally independent of this work, making the results convincing.”
SEED: Self-supervised Distillation For Visual Representation (2021, background): “The inability of models with less parameters for handling large volume of categories and data, which exists also in other domains, i.e., face recognition (Guo et al., 2016; Zhang et al., 2017).”