Megaage Dataset Download WORK

MegaFace is a large-scale public face recognition training dataset that serves as one of the most important benchmarks for commercial face recognition vendors. It includes 4,753,320 faces of 672,057 identities from 3,311,471 photos downloaded from 48,383 Flickr users' photo albums. All photos included a Creative Commons licenses, but most were not licensed for commercial use.

This analysis explores how the MegaFace face recognition dataset exploited the good intentions of Flickr users and the Creative Commons license system to advance facial recognition technologies around the world by companies including Alibaba, Amazon, Google, CyberLink, IntelliVision, N-TechLab (FindFace.pro), Mitsubishi, Orion Star Technology, Philips, Samsung 1, SenseTime, Sogou, Tencent, and Vision Semantics to name only a few. According to the press release from the University of Washington, "more than 300 research groups [were] working with MegaFace" as of 2016, including multiple law enforcement agencies.

Megaage Dataset Download

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To understand which licenses were applied to the images in the MegaFace dataset we analyzed the metadata for all 3,311,471 images from 48,383 Flickr accounts and found that 69% (2,284,369) of the images prohibited commercial use, while only 31% (1,027,102) allowed it. But all 3,311,471 images required some form of attribution, of which none was provided by the MegaFace dataset nor any of the research projects that used it. This would amount to 3,311,471 violations of Creative Commons licenses for each commercial use of the dataset if it were to be enforced.

Defining commercial use of training data is still a gray area. But the intent of the dataset is clear. According to the research paper introducing the dataset, the motivation for creating MegaFace was commercial in nature: "let's say one wishes to create an application that uses the best face recognition algorithm out there, how would they know which algorithm is better to implement or buy?" 1 In other words, how can commercial face recognition vendors prove their product is superior? Simple: they compete in open challenges, using the MegaFace dataset as a baseline for comparison with other algorithms, and then advertise the results.

According to BiometricUpdate.com, a news website for the biometrics industry, the MegaFace dataset has now become "one of the most reliable and popular frameworks of reference in assessing facial recognition performance, particularly on a massive scale". It frequently appears in press releases and promotional material for top facial recognition vendors.

The MegaFace dataset begins in 2004, the first year Flickr began offering free online photo sharing to Internet users. Since the beginning Flickr recommended and promoted Creative Commons (CC) licenses as a way to facilitate sharing and reposting images. Featured images on their homepage prominently displayed CC licensing, the majority of their licensing options were CC, and later they provided unlimited free hosting for images that used CC licenses. Their strategy worked. By 2010 Flickr had surpassed 100 million CC-licensed images.

In 2015 researchers at the University of Washington tapped YFCC100M to create the MegaFace face recognition dataset. All 4,753,320 annotated faces from 3,311,471 images in the MegaFace dataset were derived from the original YFC100M dataset. The only public dataset with a comparable number of images was Microsoft Research's MS-Celeb-1M dataset. Incidentally, MS-Celeb has since been withdrawn due to our joint investigation with the Financial Times.

In 2017, one year after the release of MegaFace, SenseTime Limited (CN) funded a new derivative dataset based on the original MegaFace dataset. Their new dataset, called MegaAge, was used to study facial age analysis. Then again in 2018, MegaFace was used to create another face dataset, called TinyFace, for the purpose of studying face recognition on low resolution imagery, such as CCTV. And yet again in 2019, the MegaFace dataset was used to create another face recognition dataset called DiveFace by a group called SensitiveNets from Madrid, which aims to "train unbiased and discrimination-aware face recognition algorithms".

Not only does MegaFace appear in an ever-growing list of research projects and derivative datasets funded and used by giant technology companies, it is also appears in patents. A 2018 patent from China called "Deep learning-based face recognition and face verification supervised learning method" (patent number CN108256450A) claims that "experimental data sets of the present invention comprises a largest face recognition database MegaFace Challenge" and that "The method of the present experiment only on MegaFace database to a data set of three gallery to test the proposed model of the present method." The figures included in the patent publication even include images from the MegaFace dataset.

According to reporting from the New York Times, the MegaFace dataset has been downloaded thousands of times by "companies and government agencies around the world", including "U.S. defense contractor Northrop Grumman; In-Q-Tel, the investment arm of the Central Intelligence Agency; ByteDance the parent company of the Chinese social media app TikTok; and the Chinese surveillance company Megvii."

What did the MegaFace download include? The downloads provided by the University of Washington included 13 compressed files including a total 855GB of imagery, a JSON file with metadata, a version of tightly-cropped faces, and two compressed files with disjoint images. The 855GB of images included in the MegaFace dataset are entirely from Flickr and entirely derivatives of the YFCC100M dataset, which is governed by Creative Commons legal requirements.

According to Creative Commons, images must include the appropriate author credit and follow the commercial or non-commercial restrictions as labeled by the author. However, the MegaFace dataset does not provide any such metadata to credit the creators. The example below shows the extent of the data included for each photo. The field labeled box corresponds to the location of the face detection box. The exp_bb field corresponds to an expanded face detection box. The landmarks field corresponds to a 68 facial landmarks describing facial features. The full_img_url is simply the direct URL to the image on Flickr.com.

The MegaFace dataset is the largest publicly available facial recognition dataset with a million faces and their respective bounding boxes. All images obtained from Flickr (Yahoo's dataset) and licensed under Creative Commons.

The dataset contains 11202 ambiguous image pairs collected from Visual Genome. Each image pair is annotated with 4.6 discriminative questions and 5.9 non-discriminative questions on average. The dataset is used in our ICCV 2017 paper "Learning to Disambiguate by Asking Discriminative Questions".

We introduce a new large-scale MegaAge dataset that consists of 41,941 faces annotated with age posterior distributions. We also provide the MegaAge-Asian dataset that consists only Asian faces (40,000 face images). The dataset is used in our BMVC 2017 paper "Quantifying Facial Age by Posterior of Age Comparisons".

The dataset contains 15 documentary films that are downloaded from YouTube, whose durations vary from 9 minutes to as long as 50 minutes, and the total number of frames is more than 747,000. More than 4000 object tracklets of 65 categories are annotated. The dataset is used in our CVPR 2017 paper "Discover and Learn New Objects from Documentaries".

To facilitate the learning of evaluation of pedestrian color naming, we build a new large-scale dataset, named Pedestrian Color Naming (PCN) dataset, which contains 14,213 images, each of which hand-labeled with color label for each pixel. All images in the PCN dataset are obtained from the Market- 1501 dataset.

WIDER ATTRIBUTE dataset is a human attribute recognition benchmark dataset, of which images are selected from the publicly available WIDER dataset. There are a total of 13789 images. We annotate a bounding box for each person in these images, but no more than 20 people (with top resolutions) in a crowd image, resulting in 57524 boxes in total and 4+ boxes per image on average. For each bounding box, we label 14 distinct human attributes, resulting in a total of 805336 labels.

General-100 dataset contains 100 bmp-format images (with no compression). We used this dataset in our FSRCNN ECCV 2016 paper. The size of these 100 images ranges from 710 x 704 (large) to 131 x 112 (small). They are all of good quality with clear edges but fewer smooth regions (e.g., sky and ocean), thus are very suitable for the super-resolution training.

WIDER FACE dataset is a face detection benchmark dataset, of which images are selected from the publicly available WIDER dataset. We choose 32,203 images and label 393,703 faces with a high degree of variability in scale, pose and occlusion as depicted in the sample images. WIDER FACE dataset is organized based on 61 event classes. For each event class, we randomly select 40%/10%/50% data as training, validation and testing sets. We adopt the same evaluation metric employed in the PASCAL VOC dataset. Similar to MALF and Caltech datasets, we do not release bounding box ground truth for the test images. Users are required to submit final prediction files, which we shall proceed to evaluate.

The dataset is used in our CVPR paper. The WWW dataset provides 10,000 videos with over 8 million frames from 8,257 diverse scenes, therefore offering a superiorly comprehensive dataset for the area of crowd understanding. The abundant sources of these videos also enrich the diversity and completeness.

The dataset is used in our CVPR paper. The Comprehensive Cars (CompCars) dataset contains data from two scenarios, including images from web-nature and surveillance-nature. The web-nature data contains 163 car makes with 1,716 car models. There are a total of 136,726 images capturing the entire cars and 27,618 images capturing the car parts. The full car images are labeled with bounding boxes and viewpoints. Each car model is labeled with five attributes, including maximum speed, displacement, number of doors, number of seats, and type of car. The surveillance-nature data contains 50,000 car images captured in the front view. 17dc91bb1f