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MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
TL;DR: 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, which could lead to one of the largest classification problems in computer vision.
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Abstract: 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.
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Figures
Fig. 1. An example of our face recognition task. Our task is to recognize the face in the image and then link this face with the corresponding entity key in the knowledge base. By recognizing the left image to be “Anne Hathaway” and linking to the entity key, we know she is an American actress born in 1982, who has played Mia Thermopolis in The Princess Diaries, not the other Anne Hathaway who was the wife of William Shakespeare. Input image is from the web. 2 ![Fig. 2. Distribution of the properties of the celebrities in our one-million list in different aspects. The large scale of our dataset naturally introduces great diversity. As shown in (a) and (b), we include persons with more than 2000 different professions, and come from more than 200 distinct countries/regions. The figure (c) demonstrates that we don’t include celebrities who were born before 1846 (long time before the first rollfilm specialized camera “Kodak” was invented [19]) and covers celebrities of a large variance of age. In (d), we notice that we have more females than males in our onemillion celebrity list. This might be correlated with the profession distribution in our list.](/figures/figure2-1-4pi5ioqhkqmp.png)
Fig. 2. Distribution of the properties of the celebrities in our one-million list in different aspects. The large scale of our dataset naturally introduces great diversity. As shown in (a) and (b), we include persons with more than 2000 different professions, and come from more than 200 distinct countries/regions. The figure (c) demonstrates that we don’t include celebrities who were born before 1846 (long time before the first rollfilm specialized camera “Kodak” was invented [19]) and covers celebrities of a large variance of age. In (d), we notice that we have more females than males in our onemillion celebrity list. This might be correlated with the profession distribution in our list. 
Fig. 4. Examples (subset) of the training images for the celebrity with entity key m.06y3r (Steve Jobs). The image marked with a green rectangle is claimed to be Steve Jobs when he was in high school. The image marked with a red rectangle is considered as a noise sample in our dataset, since it is synthesized by combining one image of Steve Jobs and one image of Ashton Kutcher, who is the actor in the movie “Jobs”. 
Fig. 3. Labeling GUI for “Chuck Palhniuk”. (partial view) As shown in the figure, in the upper right corner, a representative image and a short description is provided. For a given image candidate, judge can label as “not for this celebrity” (red), “yes for this celebrity” (green), or “broken image” (dark gray). 
Table 1. Face recognition datasets
![Fig. 2. Distribution of the properties of the celebrities in our one-million list in different aspects. The large scale of our dataset naturally introduces great diversity. As shown in (a) and (b), we include persons with more than 2000 different professions, and come from more than 200 distinct countries/regions. The figure (c) demonstrates that we don’t include celebrities who were born before 1846 (long time before the first rollfilm specialized camera “Kodak” was invented [19]) and covers celebrities of a large variance of age. In (d), we notice that we have more females than males in our onemillion celebrity list. This might be correlated with the profession distribution in our list.](/figures/figure2-1-4pi5ioqhkqmp.webp)
Citations
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- 06 Sep 2022
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3D face-model reconstruction from a single image: A feature aggregation approach using hierarchical transformer with weak supervision
TL;DR: In this article , a hierarchical transformer network is proposed to extract the 3D face parameters from a single 2D image, which can achieve comparable results to the current state-of-the-art SOTA performance.
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Deep Face Rectification for 360° Dual-Fisheye Cameras
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17
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TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
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ImageNet Large Scale Visual Recognition Challenge
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TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) has been running annually for five years (since 2010) and has become the standard benchmark for large-scale object recognition.
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