Date: Mon, 15 Apr 2019 14:08:35 +0200 Subject: fix up duke --- .../datasets/50_people_one_question/index.html | 114 +++++++++ site/public/datasets/afad/index.html | 127 ++++++++++ site/public/datasets/brainwash/index.html | 163 ++++++++++++ site/public/datasets/caltech_10k/index.html | 124 ++++++++++ site/public/datasets/celeba/index.html | 126 ++++++++++ site/public/datasets/cofw/index.html | 179 ++++++++++++++ site/public/datasets/duke_mtmc/index.html | 139 +++++++++-- site/public/datasets/feret/index.html | 87 +++++++ site/public/datasets/hrt_transgender/index.html | 2 +- site/public/datasets/lfpw/index.html | 116 +++++++++ site/public/datasets/lfw/index.html | 2 +- site/public/datasets/market_1501/index.html | 132 ++++++++++ site/public/datasets/msceleb/index.html | 4 +- site/public/datasets/oxford_town_centre/index.html | 29 ++- site/public/datasets/pipa/index.html | 120 +++++++++ site/public/datasets/pubfig/index.html | 117 +++++++++ site/public/datasets/uccs/index.html | 274 +++++++++++++++++++++ site/public/datasets/vgg_face2/index.html | 142 +++++++++++ site/public/datasets/viper/index.html | 122 +++++++++ .../public/datasets/youtube_celebrities/index.html | 113 +++++++++ 20 files changed, 2201 insertions(+), 31 deletions(-) create mode 100644 site/public/datasets/50_people_one_question/index.html create mode 100644 site/public/datasets/afad/index.html create mode 100644 site/public/datasets/brainwash/index.html create mode 100644 site/public/datasets/caltech_10k/index.html create mode 100644 site/public/datasets/celeba/index.html create mode 100644 site/public/datasets/cofw/index.html create mode 100644 site/public/datasets/feret/index.html create mode 100644 site/public/datasets/lfpw/index.html create mode 100644 site/public/datasets/market_1501/index.html create mode 100644 site/public/datasets/pipa/index.html create mode 100644 site/public/datasets/pubfig/index.html create mode 100644 site/public/datasets/uccs/index.html create mode 100644 site/public/datasets/vgg_face2/index.html create mode 100644 site/public/datasets/viper/index.html create mode 100644 site/public/datasets/youtube_celebrities/index.html (limited to 'site/public/datasets') diff --git a/site/public/datasets/50_people_one_question/index.html b/site/public/datasets/50_people_one_question/index.html new file mode 100644 index 00000000..dfd8cbff --- /dev/null +++ b/site/public/datasets/50_people_one_question/index.html @@ -0,0 +1,114 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

+ Datasets + About +

+ +

People One Question is a dataset of people from an online video series on YouTube and Vimeo used for building facial recogntion algorithms

People One Question dataset includes ... +

50 People 1 Question

[ page under development ]

Who used 50 People One Question Dataset?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

+ To help understand how 50 People One Question Dataset has been used around the world by commercial, military, and academic organizations; existing publicly available research citing 50 People One Question was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ The dataset citations used in the visualizations were collected from Semantic Scholar, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms. +

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/afad/index.html b/site/public/datasets/afad/index.html new file mode 100644 index 00000000..df14e7cd --- /dev/null +++ b/site/public/datasets/afad/index.html @@ -0,0 +1,127 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

Asian Face Age Dataset

+ +

+ Datasets + About +

+ +

Asian Face Age Dataset

[ page under development ]

Who used Asian Face Age Dataset?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

+ To help understand how Asian Face Age Dataset has been used around the world by commercial, military, and academic organizations; existing publicly available research citing The Asian Face Age Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ +

(ignore) research notes

The Asian Face Age Dataset (AFAD) is a new dataset proposed for evaluating the performance of age estimation, which contains more than 160K facial images and the corresponding age and gender labels. This dataset is oriented to age estimation on Asian faces, so all the facial images are for Asian faces. It is noted that the AFAD is the biggest dataset for age estimation to date. It is well suited to evaluate how deep learning methods can be adopted for age estimation. +Motivation
+
For age estimation, there are several public datasets for evaluating the performance of a specific algorithm, such as FG-NET [1] (1002 face images), MORPH I (1690 face images), and MORPH II[2] (55,608 face images). Among them, the MORPH II is the biggest public dataset to date. On the other hand, as we know it is necessary to collect a large scale dataset to train a deep Convolutional Neural Network. Therefore, the MORPH II dataset is extensively used to evaluate how deep learning methods can be adopted for age estimation [3][4].
+
However, the ethnic is very unbalanced for the MORPH II dataset, i.e., it has only less than 1% Asian faces. In order to evaluate the previous methods for age estimation on Asian Faces, the Asian Face Age Dataset (AFAD) was proposed.
+
There are 164,432 well-labeled photos in the AFAD dataset. It consist of 63,680 photos for female as well as 100,752 photos for male, and the ages range from 15 to 40. The distribution of photo counts for distinct ages are illustrated in the figure above. Some samples are shown in the Figure on the top. Its download link is provided in the "Download" section.
+
In addition, we also provide a subset of the AFAD dataset, called AFAD-Lite, which only contains PLACEHOLDER well-labeled photos. It consist of PLACEHOLDER photos for female as well as PLACEHOLDER photos for male, and the ages range from 15 to 40. The distribution of photo counts for distinct ages are illustrated in Fig. PLACEHOLDER. Its download link is also provided in the "Download" section.
+
The AFAD dataset is built by collecting selfie photos on a particular social network -- RenRen Social Network (RSN) [5]. The RSN is widely used by Asian students including middle school, high school, undergraduate, and graduate students. Even after leaving from school, some people still access their RSN account to connect with their old classmates. So, the age of the RSN user crosses a wide range from 15-years to more than 40-years old.
+
Please notice that this dataset is made available for academic research purpose only.
+

https://afad-dataset.github.io/

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/brainwash/index.html b/site/public/datasets/brainwash/index.html new file mode 100644 index 00000000..03331a2d --- /dev/null +++ b/site/public/datasets/brainwash/index.html @@ -0,0 +1,163 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

Brainwash Dataset

+ +

+ Datasets + About +

+ +

Brainwash is a dataset of webcam images taken from the Brainwash Cafe in San Francisco in 2014

The Brainwash dataset includes 11,918 images of "everyday life of a busy downtown cafe" and is used for training head detection surveillance algorithms +

Brainwash Dataset

Brainwash is a head detection dataset created from San Francisco's Brainwash Cafe livecam footage. It includes 11,918 images of "everyday life of a busy downtown cafe" 1 captured at 100 second intervals throught the entire day. Brainwash dataset was captured during 3 days in 2014: October 27, November 13, and November 24. According the author's reserach paper introducing the dataset, the images were acquired with the help of Angelcam.com. 2

Brainwash is not a widely used dataset but since its publication by Stanford University in 2015, it has notably appeared in several research papers from the National University of Defense Technology in Changsha, China. In 2016 and in 2017 researchers there conducted studies on detecting people's heads in crowded scenes for the purpose of surveillance. 3 4

If you happen to have been at Brainwash cafe in San Francisco at any time on October 26, November 13, or November 24 in 2014 you are most likely included in the Brainwash dataset and have unwittingly contributed to surveillance research.

Who used Brainwash Dataset?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

+ To help understand how Brainwash Dataset has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Brainwash Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ +

Supplementary Information

+ +

A visualization of 81,973 head annotations from the Brainwash dataset training partition. © megapixels.cc

An sample image from the Brainwash dataset used for training face and head detection algorithms for surveillance. The datset contains about 12,000 images. License: Open Data Commons Public Domain Dedication (PDDL)

49 of the 11,918 images included in the Brainwash dataset. License: Open Data Commons Public Domain Dedication (PDDL)

TODO

change supp images to 2x2 grid with bboxes
add bounding boxes to the header image
remake montage with randomized images, with bboxes
add ethics link to Stanford
add optout info

+ +

Cite Our Work

+ + If you use our data, research, or graphics please cite our work: + +

+@online{megapixels,
+  author = {Harvey, Adam. LaPlace, Jules.},
+  title = {MegaPixels: Origins, Ethics, and Privacy Implications of Publicly Available Face Recognition Image Datasets},
+  year = 2019,
+  url = {https://megapixels.cc/},
+  urldate = {2019-04-20}
+}

+ +

References

a
"readme.txt" https://exhibits.stanford.edu/data/catalog/sx925dc9385.
+
a
Stewart, Russel. Andriluka, Mykhaylo. "End-to-end people detection in crowded scenes". 2016.
+
a
Li, Y. and Dou, Y. and Liu, X. and Li, T. Localized Region Context and Object Feature Fusion for People Head Detection. ICIP16 Proceedings. 2016. Pages 594-598.
+
a
Zhao. X, Wang Y, Dou, Y. A Replacement Algorithm of Non-Maximum Suppression Base on Graph Clustering.
+

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/caltech_10k/index.html b/site/public/datasets/caltech_10k/index.html new file mode 100644 index 00000000..00b5e7fd --- /dev/null +++ b/site/public/datasets/caltech_10k/index.html @@ -0,0 +1,124 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

Brainwash Dataset

+ +

+ Datasets + About +

+ +

Caltech 10K Faces Dataset

[ page under development ]

Who used Brainwash Dataset?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ +

(ignore) research notes

The dataset contains images of people collected from the web by typing common given names into Google Image Search. The coordinates of the eyes, the nose and the center of the mouth for each frontal face are provided in a ground truth file. This information can be used to align and crop the human faces or as a ground truth for a face detection algorithm. The dataset has 10,524 human faces of various resolutions and in different settings, e.g. portrait images, groups of people, etc. Profile faces or very low resolution faces are not labeled.

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/celeba/index.html b/site/public/datasets/celeba/index.html new file mode 100644 index 00000000..c4caef20 --- /dev/null +++ b/site/public/datasets/celeba/index.html @@ -0,0 +1,126 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

CelebA Dataset

+ +

+ Datasets + About +

+ +

CelebA is a dataset of people...

CelebA includes... +

CelebA Dataset

[ PAGE UNDER DEVELOPMENT ]

Who used CelebA Dataset?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

+ To help understand how CelebA Dataset has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Large-scale CelebFaces Attributes Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ +

Research

"An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks" mentions use by sponsored by an agency of the United States government. Neither the United States government nor Lawrence Livermore National Security, LLC, nor any of their"
7dab6fbf42f82f0f5730fc902f72c3fb628ef2f0
principal responsibility is ensuring the safety, security and reliability of the nation's nuclear weapons NNSA ( National Nuclear Security Administration )

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/cofw/index.html b/site/public/datasets/cofw/index.html new file mode 100644 index 00000000..4851e256 --- /dev/null +++ b/site/public/datasets/cofw/index.html @@ -0,0 +1,179 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

COFW Dataset

+ +

+ Datasets + About +

+ +

Caltech Occluded Faces in the Wild

[ PAGE UNDER DEVELOPMENT ]

Who used COFW Dataset?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

+ To help understand how COFW Dataset has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Caltech Occluded Faces in the Wild was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ +

(ignore) research notes

Years

1993-1996

Images

14,126

Identities

1,199

Origin

Web Searches

Funded by

ODNI, IARPA, Microsoft

COFW is "is designed to benchmark face landmark algorithms in realistic conditions, which include heavy occlusions and large shape variations" [Robust face landmark estimation under occlusion].

We asked four people with different levels of computer vision knowledge to each collect 250 faces representative of typical real-world images, with the clear goal of challenging computer vision methods. +The result is 1,007 images of faces obtained from a variety of sources.
+

Robust face landmark estimation under occlusion

Our face dataset is designed to present faces in real-world conditions. Faces show large variations in shape and occlusions due to differences in pose, expression, use of accessories such as sunglasses and hats and interactions with objects (e.g. food, hands, microphones, etc.). All images were hand annotated in our lab using the same 29 landmarks as in LFPW. We annotated both the landmark positions as well as their occluded/unoccluded state. The faces are occluded to different degrees, with large variations in the type of occlusions encountered. COFW has an average occlusion of over 23%. +To increase the number of training images, and since COFW has the exact same landmarks as LFPW, for training we use the original non-augmented 845 LFPW faces + 500 COFW faces (1345 total), and for testing the remaining 507 COFW faces. To make sure all images had occlusion labels, we annotated occlusion on the available 845 LFPW training images, finding an average of only 2% occlusion.
+

http://www.vision.caltech.edu/xpburgos/ICCV13/

This research is supported by NSF Grant 0954083 and by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via IARPA R&D Contract No. 2014-14071600012.
+

https://www.cs.cmu.edu/~peiyunh/topdown/

+ +

Biometric Trade Routes

+ +

+ To help understand how COFW Dataset has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Caltech Occluded Faces in the Wild was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the location markers to reveal research projects at that location. +

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org and then dataset usage verified and geolocated.

+ +

Supplementary Information

+ +

Dataset Citations

+ +

Who used COFW Dataset?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

TODO

- replace graphic

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/duke_mtmc/index.html b/site/public/datasets/duke_mtmc/index.html index 9bec47ed..ba32484a 100644 --- a/site/public/datasets/duke_mtmc/index.html +++ b/site/public/datasets/duke_mtmc/index.html @@ -27,7 +27,7 @@

Duke MTMC is a dataset of surveillance camera footage of students on Duke University campus

Duke MTMC contains over 2 million video frames and 2,700 unique identities collected from 8 HD cameras at Duke University campus in March 2014 -

Duke MTMC

The Duke Multi-Target, Multi-Camera Tracking Dataset (MTMC) is a dataset of video recorded on Duke University campus for research and development of networked camera surveillance systems. MTMC tracking is used for citywide dragnet surveillance systems such as those used throughout China by SenseTime 1 and the oppressive monitoring of 2.5 million Uyghurs in Xinjiang by SenseNets 2. In fact researchers from both SenseTime 4 5 and SenseNets 3 used the Duke MTMC dataset for their research.

The Duke MTMC dataset is unique because it is the largest publicly available MTMC and person re-identification dataset and has the longest duration of annotated video. In total, the Duke MTMC dataset provides over 14 hours of 1080p video from 8 synchronized surveillance cameras. 6 It is among the most widely used person re-identification datasets in the world. The approximately 2,700 unique people in the Duke MTMC videos, most of whom are students, are used for research and development of surveillance technologies by commercial, academic, and even defense organizations.

A collection of 1,600 out of the 2,700 students and passersby captured into the Duke MTMC surveillance research dataset. These students were also included in the Duke MTMC Re-ID dataset extension used for person re-identification. © megapixels.cc

The creation and publication of the Duke MTMC dataset in 2016 was originally funded by the U.S. Army Research Laboratory and the National Science Foundation 6. Since 2016 use of the Duke MTMC dataset images have been publicly acknowledged in research funded by or on behalf of the Chinese National University of Defense 7 8, IARPA and IBM 9, and U.S. Department of Homeland Security 10.

The 8 cameras deployed on Duke's campus were specifically setup to capture students "during periods between lectures, when pedestrian traffic is heavy". 6 Camera 7 and 2 capture large groups of prospective students and children. Camera 5 was positioned to capture students as they enter and exit Duke University's main chapel. Each camera's location is documented below.

Duke MTMC camera locations on Duke University campus © megapixels.cc

Duke MTMC camera views for 8 cameras deployed on campus © megapixels.cc

Duke MTMC pedestrian detection saliency maps for 8 cameras deployed on campus © megapixels.cc

Duke MTMC (Multi-Target, Multi-Camera Tracking) is a dataset of video recorded on Duke University campus for research and development of networked camera surveillance systems. MTMC tracking algorithms are used for citywide dragnet surveillance systems such as those used throughout China by SenseTime 1 and the oppressive monitoring of 2.5 million Uyghurs in Xinjiang by SenseNets 2. In fact researchers from both SenseTime 4 5 and SenseNets 3 used the Duke MTMC dataset for their research.

In this investigation into the Duke MTMC dataset, we found that researchers at Duke Univesity in Durham, North Carolina captured over 2,000 students, faculty members, and passersby into one of the most prolific public surveillance research datasets that's used around the world by commercial and defense surveillance organizations.

Since it's publication in 2016, the Duke MTMC dataset has been used in over 100 studies at organizations around the world including SenseTime 4 5, SenseNets 3, IARPA and IBM 9, Chinese National University of Defense 7 8, US Department of Homeland Security 10, Tencent, Microsoft, Microsft Asia, Fraunhofer, Senstar Corp., Alibaba, Naver Labs, Google and Hewlett-Packard Labs to name only a few.

The creation and publication of the Duke MTMC dataset in 2014 (published in 2016) was originally funded by the U.S. Army Research Laboratory and the National Science Foundation 6. Though our analysis of the geographic locations of the publicly available research shows over twice as many citations by researchers from China (44% China, 20% United States). In 2018 alone, there were 70 research project citations from China.

A collection of 1,600 out of the 2,700 students and passersby captured into the Duke MTMC surveillance research and development dataset on . These students were also included in the Duke MTMC Re-ID dataset extension used for person re-identification. Open Data Commons Attribution License.

The 8 cameras deployed on Duke's campus were specifically setup to capture students "during periods between lectures, when pedestrian traffic is heavy". 6. Camera 5 was positioned to capture students as entering and exiting the university's main chapel. Each camera's location and approximate field of view. The heat map visualization shows the locations where pedestrians were most frequently annotated in each video from the Duke MTMC datset.

Duke MTMC camera locations on Duke University campus. Open Data Commons Attribution License.

Duke MTMC camera views for 8 cameras deployed on campus © megapixels.cc

Duke MTMC pedestrian detection saliency maps for 8 cameras deployed on campus © megapixels.cc

Who used Duke MTMC Dataset?

@@ -110,18 +111,122 @@

Supplementary Information

Notes

The Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812

References

a
https://qz.com/1248493/sensetime-the-billion-dollar-alibaba-backed-ai-company-thats-quietly-watching-everyone-in-china/
+

Funding

Original funding for the Duke MTMC dataset was provided by the Army Research Office under Grant No. W911NF-10-1-0387 and by the National Science Foundation +under Grants IIS-10-17017 and IIS-14-20894.

Video Timestamps

The video timestamps contain the likely, but not yet confirmed, date and times of capture. Because the video timestamps align with the start and stop time sync data provided by the researchers, it at least aligns the relative time. The rainy weather on that day also contribute towards the likelihood of March 14, 2014..

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Camera	Date	Start	End
Camera 1	March 14, 2014	4:14PM	5:43PM
Camera 2	March 14, 2014	4:13PM	4:43PM
Camera 3	March 14, 2014	4:20PM	5:48PM
Camera 4	March 14, 2014	4:21PM	5:54PM

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Camera	Date	Start	End
Camera 5	March 14, 2014	4:12PM	5:43PM
Camera 6	March 14, 2014	4:18PM	5:43PM
Camera 7	March 14, 2014	4:16PM	5:40PM
Camera 8	March 14, 2014	4:25PM	5:42PM

Opting Out

If you attended Duke University and were captured by any of the 8 surveillance cameras positioned on campus in 2014, there is unfortunately no way to be removed. The dataset files have been distributed throughout the world and it would not be possible to contact all the owners for removal. Nor do the authors provide any options for students to opt-out, nor did they even inform students they would be used at test subjects for surveillance research and development in a project funded, in part, by the United States Army Research Office.

Notes

The Duke MTMC dataset paper mentions 2,700 identities, but their ground truth file only lists annotations for 1,812

+ +

Cite Our Work

+ + If you use our data, research, or graphics please cite our work: + +

+@online{megapixels,
+  author = {Harvey, Adam. LaPlace, Jules.},
+  title = {MegaPixels: Origins, Ethics, and Privacy Implications of Publicly Available Face Recognition Image Datasets},
+  year = 2019,
+  url = {https://megapixels.cc/},
+  urldate = {2019-04-20}
+}

+ +

If you use any data from the Duke MTMC please follow their license and cite their work as:

+@inproceedings{ristani2016MTMC,
+ title =        {Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking},
+ author =       {Ristani, Ergys and Solera, Francesco and Zou, Roger and Cucchiara, Rita and Tomasi, Carlo},
+ booktitle =    {European Conference on Computer Vision workshop on Benchmarking Multi-Target Tracking},
+ year =         {2016}
+}
+

References

a
https://qz.com/1248493/sensetime-the-billion-dollar-alibaba-backed-ai-company-thats-quietly-watching-everyone-in-china/
a
https://foreignpolicy.com/2019/03/19/962492-orwell-china-socialcredit-surveillance/
-
a
"Attention-Aware Compositional Network for Person Re-identification". 2018. Source
-
a
"End-to-End Deep Kronecker-Product Matching for Person Re-identification". 2018. source
-
a
"Person Re-identification with Deep Similarity-Guided Graph Neural Network". 2018. Source
-
a b c
"Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking". 2016. Source
-
a
"Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers". 2018. Source
-
a
"Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks". 2018. Source
-
a
"Horizontal Pyramid Matching for Person Re-identification". 2019. Source
-
a
"Re-Identification with Consistent Attentive Siamese Networks". 2018. Source
+
a b
"Attention-Aware Compositional Network for Person Re-identification". 2018. SemanticScholar, PDF
+
a b
"End-to-End Deep Kronecker-Product Matching for Person Re-identification". 2018. SemanticScholar, PDF
+
a b
"Person Re-identification with Deep Similarity-Guided Graph Neural Network". 2018. SemanticScholar
+
a b
"Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking". 2016. SemanticScholar
+
a
"Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers". 2018. SemanticScholar
+
a
"Unsupervised Multi-Object Detection for Video Surveillance Using Memory-Based Recurrent Attention Networks". 2018. SemanticScholar
+
a
"Horizontal Pyramid Matching for Person Re-identification". 2019. SemanticScholar
+
a
"Re-Identification with Consistent Attentive Siamese Networks". 2018. SemanticScholar

diff --git a/site/public/datasets/feret/index.html b/site/public/datasets/feret/index.html new file mode 100644 index 00000000..089cd351 --- /dev/null +++ b/site/public/datasets/feret/index.html @@ -0,0 +1,87 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

LFW

+ +

+ Datasets + About +

+ +

Published

2007

Images

13,233

Identities

5,749

Purpose

face recognition

Website

umass.edu

FacE REcognition Dataset (FERET)

[ page under development ]

{% include 'dashboard.html' %}

(ignore) RESEARCH below this line

Years: 1993-1996
Images: 14,126
Identities: 1,199
Origin: Fairfax, MD
Facial Recognition Evaluation (FERET) is develop, test, and evaluate face recognition algorithms
The goal of the FERET program was to develop automatic face recognition capabilities that could be employed to assist security, intelligence, and law enforcement personnel in the performance of their duties.
https://www.nist.gov/programs-projects/face-recognition-technology-feret

"The FERET database and evaluation procedure for face-recognition algorithms"

Images were captured using Kodak Ultra film
The facial images were collected in 11 sessions from August 1993 to December 1994. Conducted at George Mason University and at US Army Research Laboratory facilities,

FERET (Face Recognition Technology) Recognition Algorithm Development and Test Results

"A release form is necessary because of the privacy laws in the United States."

Funding

The FERET program is sponsored by the U.S. Depart- ment of Defense’s Counterdrug Technology Development Program Office. The U.S. Army Research Laboratory (ARL) is the technical agent for the FERET program. ARL designed, administered, and scored the FERET tests. George Mason University collected, processed, and main- tained the FERET database. Inquiries regarding the FERET database or test should be directed to P. Jonathon Phillips.

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/hrt_transgender/index.html b/site/public/datasets/hrt_transgender/index.html index 486b9122..231a5271 100644 --- a/site/public/datasets/hrt_transgender/index.html +++ b/site/public/datasets/hrt_transgender/index.html @@ -27,7 +27,7 @@

TBD

TBD -

Published

2013

diff --git a/site/public/datasets/lfpw/index.html b/site/public/datasets/lfpw/index.html new file mode 100644 index 00000000..f734d332 --- /dev/null +++ b/site/public/datasets/lfpw/index.html @@ -0,0 +1,116 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

LFWP

+ +

+ Datasets + About +

+ +

Labeled Face Parts in The Wild

Who used LFWP?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

+ To help understand how LFWP has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Labeled Face Parts in the Wild was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ +

RESEARCH below this line

Release 1 of LFPW consists of 1,432 faces from images downloaded from the web using simple text queries on sites such as google.com, flickr.com, and yahoo.com. Each image was labeled by three MTurk workers, and 29 fiducial points, shown below, are included in dataset. LFPW was originally described in the following publication:
+
Due to copyright issues, we cannot distribute image files in any format to anyone. Instead, we have made available a list of image URLs where you can download the images yourself. We realize that this makes it impossible to exactly compare numbers, as image links will slowly disappear over time, but we have no other option. This seems to be the way other large web-based databases seem to be evolving.
+

https://neerajkumar.org/databases/lfpw/

This research was performed at Kriegman-Belhumeur Vision Technologies and was funded by the CIA through the Office of the Chief Scientist. https://www.cs.cmu.edu/~peiyunh/topdown/ (nk_cvpr2011_faceparts.pdf)
+

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/lfw/index.html b/site/public/datasets/lfw/index.html index 60a6bf0e..2d91b065 100644 --- a/site/public/datasets/lfw/index.html +++ b/site/public/datasets/lfw/index.html @@ -27,7 +27,7 @@

Labeled Faces in The Wild (LFW) is the first facial recognition dataset created entirely from online photos

It includes 13,456 images of 4,432 people's images copied from the Internet during 2002-2004 and is the most frequently used dataset in the world for benchmarking face recognition algorithms. -

Published

2007

diff --git a/site/public/datasets/market_1501/index.html b/site/public/datasets/market_1501/index.html new file mode 100644 index 00000000..72807efc --- /dev/null +++ b/site/public/datasets/market_1501/index.html @@ -0,0 +1,132 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

Market 1501

+ +

+ Datasets + About +

+ +

Market-1501 is a dataset is collection of CCTV footage from Tsinghua University

The Market-1501 dataset includes 1,261 people from 5 HD surveillance cameras located on campus +

Market-1501 Dataset

[ PAGE UNDER DEVELOPMENT]

Who used Market 1501?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

+ To help understand how Market 1501 has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Market 1501 Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ +

(ignore) research Notes

"MARS is an extension of the Market-1501 dataset. During collection, we placed six near synchronized cameras in the campus of Tsinghua university. There were Five 1,0801920 HD cameras and one 640480 SD camera. MARS consists of 1,261 different pedestrians whom are captured by at least 2 cameras. Given a query tracklet, MARS aims to retrieve tracklets that contain the same ID." - main paper
bbox "0065C1T0002F0016.jpg", "0065" is the ID of the pedestrian. "C1" denotes the first +camera (there are totally 6 cameras). "T0002" means the 2th tracklet. "F016" is the 16th frame +within this tracklet. For the tracklets, their names are accumulated for each ID; but for frames, +they start from "F001" in each tracklet.

@proceedings{zheng2016mars, +title={MARS: A Video Benchmark for Large-Scale Person Re-identification}, +author={Zheng, Liang and Bie, Zhi and Sun, Yifan and Wang, Jingdong and Su, Chi and Wang, Shengjin and Tian, Qi}, +booktitle={European Conference on Computer Vision}, +year={2016}, +organization={Springer} +}

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/msceleb/index.html b/site/public/datasets/msceleb/index.html index cf3a654f..be21280c 100644 --- a/site/public/datasets/msceleb/index.html +++ b/site/public/datasets/msceleb/index.html @@ -27,7 +27,7 @@

MS Celeb is a dataset of web images used for training and evaluating face recognition algorithms

The MS Celeb dataset includes over 10,000,000 images and 93,000 identities of semi-public figures collected using the Bing search engine -

Microsoft Celeb Dataset (MS Celeb)

[ PAGE UNDER DEVELOPMENT ]

https://www.hrw.org/news/2019/01/15/letter-microsoft-face-surveillance-technology

https://www.scmp.com/tech/science-research/article/3005733/what-you-need-know-about-sensenets-facial-recognition-firm

Who used Microsoft Celeb?

diff --git a/site/public/datasets/oxford_town_centre/index.html b/site/public/datasets/oxford_town_centre/index.html index 63dc52d4..af020855 100644 --- a/site/public/datasets/oxford_town_centre/index.html +++ b/site/public/datasets/oxford_town_centre/index.html @@ -27,7 +27,7 @@

Oxford Town Centre is a dataset of surveillance camera footage from Cornmarket St Oxford, England

The Oxford Town Centre dataset includes approximately 2,200 identities and is used for research and development of face recognition systems -

Published

2009

@@ -115,16 +115,23 @@

The street location of the camera used for the Oxford Town Centre dataset was confirmed by matching the road, benches, and store signs source. At that location, two public CCTV cameras exist mounted on the side of the Northgate House building at 13-20 Cornmarket St. Because of the lower camera's mounting pole directionality, a view from a private camera in the building across the street can be ruled out because it would have to show more of silhouette of the lower camera's mounting pole. Two options remain: either the public CCTV camera mounted to the side of the building was used or the researchers mounted their own camera to the side of the building in the same location. Because the researchers used many other existing public CCTV cameras for their research projects it is likely that they would also be able to access to this camera.

To discredit the theory that this public CCTV is only seen pointing the other way in Google Street View images, at least one public photo shows the upper CCTV camera pointing in the same direction as the Oxford Town Centre dataset proving the camera can and has been rotated before.

As for the capture date, the text on the storefront display shows a sale happening from December 2nd – 7th indicating the capture date was between or just before those dates. The capture year is either 2008 or 2007 since prior to 2007 the Carphone Warehouse (photo, history) did not exist at this location. Since the sweaters in the GAP window display are more similar to those in a GAP website snapshot from November 2007, our guess is that the footage was obtained during late November or early December 2007. The lack of street vendors and slight waste residue near the bench suggests that is was probably a weekday after rubbish removal.

Footage from this public CCTV camera was used to create the Oxford Town Centre dataset. Image sources: Google Street View (<a href="https://www.google.com/maps/@51.7528162,-1.2581152,3a,50.3y,310.59h,87.23t/data=!3m7!1e1!3m5!1s3FsGN-PqYC-VhQGjWgmBdQ!2e0!5s20120601T000000!7i13312!8i6656">map</a>)

Footage from this public CCTV camera was used to create the Oxford Town Centre dataset. Image sources: Google Street View (map)

Heat map body visualization of the pedestrians detected in the Oxford Town Centre dataset © megapixels.cc

Heat map face visualization of the pedestrians detected in the Oxford Town Centre dataset © megapixels.cc

Demo Videos Using Oxford Town Centre Dataset

Several researchers have posted their demo videos using the Oxford Town Centre dataset on YouTube:

Multi target tracking on Oxford Dataset
[Multi-pedestrian tracking (TownCentre dataset)]https://www.youtube.com/watch?v=nO-3EM9dEd4
Multiple object tracking with kalman tracker and sort
Multi target tracking on Oxford dataset
towncentre
VTD - towncenter.avi

Footage from this public CCTV camera was used to create the Oxford Town Centre dataset. Image sources: Google Street View (map)

Heat map body visualization of the pedestrians detected in the Oxford Town Centre dataset © megapixels.cc

Heat map face visualization of the pedestrians detected in the Oxford Town Centre dataset © megapixels.cc

+ +

Cite Our Work

+ + If you use our data, research, or graphics please cite our work: + +

+@online{megapixels,
+  author = {Harvey, Adam. LaPlace, Jules.},
+  title = {MegaPixels: Origins, Ethics, and Privacy Implications of Publicly Available Face Recognition Image Datasets},
+  year = 2019,
+  url = {https://megapixels.cc/},
+  urldate = {2019-04-20}
+}

+ +

References

a
Benfold, Ben and Reid, Ian. "Stable Multi-Target Tracking in Real-Time Surveillance Video". CVPR 2011. Pages 3457-3464.
a
"Guiding Visual Surveillance by Tracking Human Attention". 2009.

diff --git a/site/public/datasets/pipa/index.html b/site/public/datasets/pipa/index.html new file mode 100644 index 00000000..780b3029 --- /dev/null +++ b/site/public/datasets/pipa/index.html @@ -0,0 +1,120 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

PIPA Dataset

+ +

+ Datasets + About +

+ +

People in Photo Albums (PIPA) is a dataset...

[ add subdescrition ] +

People in Photo Albums

[ PAGE UNDER DEVELOPMENT ]

Who used PIPA Dataset?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

+ To help understand how PIPA Dataset has been used around the world by commercial, military, and academic organizations; existing publicly available research citing People in Photo Albums Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/pubfig/index.html b/site/public/datasets/pubfig/index.html new file mode 100644 index 00000000..2c8bd7b1 --- /dev/null +++ b/site/public/datasets/pubfig/index.html @@ -0,0 +1,117 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

PubFig

+ +

+ Datasets + About +

+ +

PubFig is a dataset...

[ add subdescrition ] +

PubFig

[ PAGE UNDER DEVELOPMENT ]

Who used PubFig?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

+ To help understand how PubFig has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Public Figures Face Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/uccs/index.html b/site/public/datasets/uccs/index.html new file mode 100644 index 00000000..4a0dfb5e --- /dev/null +++ b/site/public/datasets/uccs/index.html @@ -0,0 +1,274 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

UCCS

+ +

+ Datasets + About +

+ +

UnConstrained College Students is a dataset of long-range surveillance photos of students at University of Colorado in Colorado Springs

The UnConstrained College Students dataset includes 16,149 images and 1,732 identities of subjects on University of Colorado Colorado Springs campus and is used for making face recognition and face detection algorithms +

UnConstrained College Students

[ page under development ]

UnConstrained College Students (UCCS) is a dataset of long-range surveillance photos captured at University of Colorado Colorado Springs. According to the authors of two papers associated with the dataset, subjects were "photographed using a long-range high-resolution surveillance camera without their knowledge" 2. To create the dataset, the researchers used a Canon 7D digital camera fitted with a Sigma 800mm telephoto lens and photographed students 150–200m away through their office window. Photos were taken during the morning and afternoon while students were walking to and from classes. The primary uses of this dataset are to train, validate, and build recognition and face detection algorithms for realistic surveillance scenarios.

What makes the UCCS dataset unique is that it includes the highest resolution images of any publicly available face recognition dataset discovered so far (18MP), that it was captured on a campus without consent or awareness using a long-range telephoto lens, and that it was funded by United States defense and intelligence agencies.

Combined funding sources for the creation of the initial and final release of the dataset include ODNI (Office of Director of National Intelligence), IARPA (Intelligence Advance Research Projects Activity), ONR MURI (Office of Naval Research and The Department of Defense Multidisciplinary University Research Initiative), Army SBIR (Small Business Innovation Research), SOCOM SBIR (Special Operations Command and Small Business Innovation Research), and the National Science Foundation. 1 2

In 2017 the UCCS face dataset was used for a defense and intelligence agency funded face recognition challenge at the International Joint Biometrics Conference in Denver, CO. And in 2018 the dataset was used for the 2nd Unconstrained Face Detection and Open Set Recognition Challenge at the European Computer Vision Conference (ECCV) in Munich, Germany. Additional research projects that have used the UCCS dataset are included below in the list of verified citations.

UCCS is part of the IARAP Janus team https://vast.uccs.edu/project/iarpa-janus/

https://arxiv.org/abs/1708.02337

Who used UCCS?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

+ To help understand how UCCS has been used around the world by commercial, military, and academic organizations; existing publicly available research citing UnConstrained College Students Dataset was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ +

Supplementary Information

+ +

Dates and Times

The images in UCCS were taken on 18 non-consecutive days during 2012–2013. Analysis of the EXIF data embedded in original images reveal that most of the images were taken on Tuesdays, and the most frequent capture time throughout the week was 12:30PM.

UCCS photos captured per weekday © megapixels.cc

UCCS photos captured per 10-minute intervals per weekday © megapixels.cc

UCCS photos taken in 2012

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Date	Photos
Feb 23, 2012	132
March 6, 2012	288
March 8, 2012	506
March 13, 2012	160
March 20, 2012	1,840
March 22, 2012	445
April 3, 2012	1,639
April 12, 2012	14
April 17, 2012	19
April 24, 2012	63
April 25, 2012	11
April 26, 2012	20

UCCS photos taken in 2013

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Date	Photos
Jan 28, 2013	1,056
Jan 29, 2013	1,561
Feb 13, 2013	739
Feb 19, 2013	723
Feb 20, 2013	965
Feb 26, 2013	736

Location

The location of the camera and subjects can confirmed using several visual cues in the dataset images: the unique pattern of the sidewalk that is only used on the UCCS Pedestrian Spine near the West Lawn, the two UCCS sign poles with matching graphics still visible in Google Street View, the no parking sign and directionality of its arrow, the back of street sign next to it, the slight bend in the sidewalk, the presence of cars passing in the background of the image, and the far wall of the parking garage all match images in the dataset. The original papers also provides another clue: a picture of the camera inside the office that was used to create the dataset. The window view in this image provides another match for the brick pattern on the north facade of the Kraember Family Library and the green metal fence along the sidewalk. View the location on Google Maps

Location on campus where students were unknowingly photographed with a telephoto lens to be used for defense and intelligence agency funded research on face recognition. Image: Google Maps

3D view showing the angle of view of the surveillance camera used for UCCS dataset. Image: Google Maps

Funding

The UnConstrained College Students dataset is associated with two main research papers: "Large Scale Unconstrained Open Set Face Database" and "Unconstrained Face Detection and Open-Set Face Recognition Challenge". Collectively, these papers and the creation of the dataset have received funding from the following organizations:

ONR (Office of Naval Research) MURI (The Department of Defense Multidisciplinary University Research Initiative) grant N00014-08-1-0638
Army SBIR (Small Business Innovation Research) grant W15P7T-12-C-A210
SOCOM (Special Operations Command) SBIR (Small Business Innovation Research) grant H92222-07-P-0020
National Science Foundation Grant IIS-1320956
ODNI (Office of Director of National Intelligence)
IARPA (Intelligence Advance Research Projects Activity) R&D contract 2014-14071600012

Opting Out

If you attended University of Colorado Colorado Springs and were captured by the long range surveillance camera used to create this dataset, there is unfortunately currently no way to be removed. The authors do not provide any options for students to opt-out nor were students informed they would be used for training face recognition. According to the authors, the lack of any consent or knowledge of participation is what provides part of the value of Unconstrained College Students Dataset.

Ethics

Please direct any questions about the ethics of the dataset to the University of Colorado Colorado Springs Ethics and Compliance Office
For further technical information about the dataset, visit the UCCS dataset project page.

Downloads

Download EXIF data for UCCS photos: uccs_camera_exif.csv

+ +

Cite Our Work

+ + If you use our data, research, or graphics please cite our work: + +

+@online{megapixels,
+  author = {Harvey, Adam. LaPlace, Jules.},
+  title = {MegaPixels: Origins, Ethics, and Privacy Implications of Publicly Available Face Recognition Image Datasets},
+  year = 2019,
+  url = {https://megapixels.cc/},
+  urldate = {2019-04-20}
+}

+ +

References

a
Sapkota, Archana and Boult, Terrance. "Large Scale Unconstrained Open Set Face Database." 2013.
+
a b
Günther, M. et. al. "Unconstrained Face Detection and Open-Set Face Recognition Challenge," 2018. Arxiv 1708.02337v3.
+

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/vgg_face2/index.html b/site/public/datasets/vgg_face2/index.html new file mode 100644 index 00000000..75d73824 --- /dev/null +++ b/site/public/datasets/vgg_face2/index.html @@ -0,0 +1,142 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

Brainwash Dataset

+ +

+ Datasets + About +

+ +

VGG Face 2

[ page under development ]

Who used Brainwash Dataset?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ +

(ignore) research notes

The VGG Face 2 dataset includes approximately 1,331 actresses, 139 presidents, 16 wives, 3 husbands, 2 snooker player, and 1 guru
The original VGGF2 name list has been updated with the results returned from Google Knowledge
Names with a similarity score greater than 0.75 where automatically updated. Scores computed using import difflib; seq = difflib.SequenceMatcher(a=a.lower(), b=b.lower()); score = seq.ratio()
The 97 names with a score of 0.75 or lower were manually reviewed and includes name changes validating using Wikipedia.org results for names such as "Bruce Jenner" to "Caitlyn Jenner", spousal last-name changes, and discretionary changes to improve search results such as combining nicknames with full name when appropriate, for example changing "Aleksandar Petrović" to "Aleksandar 'Aco' Petrović" and minor changes such as "Mohammad Ali" to "Muhammad Ali"
The 'Description' text was automatically added when the Knowledge Graph score was greater than 250

TODO

create name list, and populate with Knowledge graph information like LFW
make list of interesting number stats, by the numbers
make list of interesting important facts
write intro abstract
write analysis of usage
find examples, citations, and screenshots of useage
find list of companies using it for table
create montages of the dataset, like LFW
create right to removal information

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/viper/index.html b/site/public/datasets/viper/index.html new file mode 100644 index 00000000..5b3ac35b --- /dev/null +++ b/site/public/datasets/viper/index.html @@ -0,0 +1,122 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

VIPeR

+ +

+ Datasets + About +

+ +

VIPeR is a person re-identification dataset of images captured at UC Santa Cruz in 2007

VIPeR contains 1,264 images and 632 persons on the UC Santa Cruz campus and is used to train person re-identification algorithms for surveillance +

VIPeR Dataset

[ page under development ]

VIPeR (Viewpoint Invariant Pedestrian Recognition) is a dataset of pedestrian images captured at University of California Santa Cruz in 2007. Accoriding to the reserachers 2 "cameras were placed in different locations in an academic setting and subjects were notified of the presence of cameras, but were not coached or instructed in any way."

VIPeR is amongst the most widely used publicly available person re-identification datasets. In 2017 the VIPeR dataset was combined into a larger person re-identification created by the Chinese University of Hong Kong called PETA (PEdesTrian Attribute).

Who used VIPeR?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

+ To help understand how VIPeR has been used around the world by commercial, military, and academic organizations; existing publicly available research citing Viewpoint Invariant Pedestrian Recognition was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ +

+ + + + + \ No newline at end of file diff --git a/site/public/datasets/youtube_celebrities/index.html b/site/public/datasets/youtube_celebrities/index.html new file mode 100644 index 00000000..39670c19 --- /dev/null +++ b/site/public/datasets/youtube_celebrities/index.html @@ -0,0 +1,113 @@ + + + + MegaPixels + + + + + + + + + + + +

+ +

MegaPixels

YouTube Celebrities

+ +

+ Datasets + About +

+ +

YouTube Celebrities

[ page under development ]

Who used YouTube Celebrities?

+ +

+ This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries. +

+ +

Biometric Trade Routes

+ +

+ To help understand how YouTube Celebrities has been used around the world by commercial, military, and academic organizations; existing publicly available research citing YouTube Celebrities was collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal research projects at that location. +

+ +

Academic
Commercial
Military / Government

Citation data is collected using SemanticScholar.org then dataset usage verified and geolocated.

+ + +

+ +

Dataset Citations

+ +

Notes...

Selected dataset sequences: (a) MBGC, (b) CMU MoBo, (c) First +Honda/UCSD, and (d) YouTube Celebrities.
This research is supported by the Central Intelligence Agency, the Biometrics +Task Force and the Technical Support Working Group through US Army contract +W91CRB-08-C-0093. The opinions, (cid:12)ndings, and conclusions or recommendations +expressed in this publication are those of the authors and do not necessarily re(cid:13)ect +the views of our sponsors.
in "Face Recognition From Video Draft 17"
International Journal of Pattern Recognition and Artifcial Intelligence WorldScientific Publishing Company

+ +

+ + + + + \ No newline at end of file -- cgit v1.2.3-70-g09d2