6 files changed, 81 insertions, 7 deletions
diff --git a/site/assets/css/css.css b/site/assets/css/css.css
index cd16409a..0ee8a4f3 100644
--- a/site/assets/css/css.css
+++ b/site/assets/css/css.css
@@ -884,7 +884,7 @@ ul.map-legend li.source:before {
   font-family: Roboto, sans-serif;
   font-weight: 400;
   background: #202020;
-  padding: 15px;
+  padding: 20px;
   margin: 10px;
 }
 .columns .column:first-of-type {
@@ -937,7 +937,7 @@ ul.map-legend li.source:before {
   margin:0 0 0 40px;
 }
 .content-about .team-member p{
-  font-size:14px;
+  font-size:16px;
 }
 .content-about .team-member img{
   margin:0;
diff --git a/site/content/pages/datasets/index.md b/site/content/pages/datasets/index.md
index 2e943fbe..c0373d60 100644
--- a/site/content/pages/datasets/index.md
+++ b/site/content/pages/datasets/index.md
@@ -13,4 +13,4 @@ sync: false
 
 # Facial Recognition Datasets
 
-### Survey
+Explore publicly available facial recognition datasets. More datasets will be added throughout 2019.
diff --git a/site/content/pages/datasets/uccs/index.md b/site/content/pages/datasets/uccs/index.md
index b3d16c2e..e0925e07 100644
--- a/site/content/pages/datasets/uccs/index.md
+++ b/site/content/pages/datasets/uccs/index.md
@@ -3,8 +3,7 @@
 status: published
 title: Unconstrained College Students
 desc: <span class="dataset-name">Unconstrained College Students (UCCS)</span> is a dataset of long-range surveillance photos of students taken without their knowledge
-subdesc: The UCCS dataset includes 16,149 images and 1,732 identities, is used for face recognition and face detection, and funded was several US defense agences
-slug: uccs
+subdesc: The UCCS dataset includes 16,149 images and 1,732 identities of students at University of Colorado Colorado Springs campus and is used for face recognition and face detection
 cssclass: dataset
 image: assets/background.jpg
 published: 2019-2-23
diff --git a/site/content/pages/research/01_from_1_to_100_pixels/index.md b/site/content/pages/research/01_from_1_to_100_pixels/index.md
index a7b863a9..b219dffb 100644
--- a/site/content/pages/research/01_from_1_to_100_pixels/index.md
+++ b/site/content/pages/research/01_from_1_to_100_pixels/index.md
@@ -56,3 +56,55 @@ Ideas:
 
 - "Note that we only keep the images with a minimal side length of 80 pixels." and "a face will be labeled as “Ignore” if it is very difficult to be detected due to blurring, severe deformation and unrecognizable eyes, or the side length of its bounding box is less than 32 pixels." Ge_Detecting_Masked_Faces_CVPR_2017_paper.pdf 
 - IBM DiF: "Faces with region size less than 50x50 or inter-ocular distance of less than 30 pixels were discarded. Faces with non-frontal pose, or anything beyond being slightly tilted to the left or the right, were also discarded."
+
+
+
+
+As the resolution
+formatted as rectangular databases of 16 bit RGB-tuples or 8 bit grayscale values 
+
+
+To consider how visual privacy applies to real world surveillance situations, the first 
+
+A single 8-bit grayscale pixel with 256 values is enough to represent the entire alphabet `a-Z0-9` with room to spare. 
+
+A 2x2 pixels contains
+
+Using no more than a 42 pixel (6x7 image) face image researchers [cite] were able to correctly distinguish between a group of 50 people. Yet 
+
+The likely outcome of face recognition research is that more data is needed to improve. Indeed, resolution is the determining factor for all biometric systems, both as training data to increase 
+
+Pixels, typically considered the buiding blocks of images and vidoes, can also be plotted as a graph of sensor values corresponding to the intensity of RGB-calibrated sensors.
+
+
+Wi-Fi and cameras presents elevated risks for transmitting videos and image documentation from conflict zones, high-risk situations, or even sharing on social media. How can new developments in computer vision also be used in reverse, as a counter-forensic tool, to minimize an individual's privacy risk?
+
+As the global Internet becomes increasingly effecient at turning the Internet into a giant dataset for machine learning, forensics, and data analysing, it would be prudent to also consider tools for decreasing the resolution. The Visual Defense module is just that. What are new ways to minimize the adverse effects of surveillance by dulling the blade. For example, a researcher paper showed that by decreasing a face size to 12x16 it was possible to do 98% accuracy with 50 people. This is clearly an example of 
+
+This research module, tentatively called Visual Defense Tools, aims to explore the 
+
+
+### Prior Research
+
+- MPI visual privacy advisor
+- NIST: super resolution
+- YouTube blur tool
+- WITNESS: blur tool
+- Pixellated text 
+- CV Dazzle
+- Bellingcat guide to geolocation
+- Peng! magic passport
+
+### Notes
+
+- In China, out of the approximately 200 million surveillance cameras only about 15% have enough resolution for face recognition. 
+- In Apple's FaceID security guide, the probability of someone else's face unlocking your phone is 1 out of 1,000,000. 
+- In England, the Metropolitan Police reported a false-positive match rate of 98% when attempting to use face recognition to locate wanted criminals. 
+- In a face recognition trial at Berlin's Sudkreuz station, the false-match rate was 20%. 
+
+
+What all 3 examples illustrate is that face recognition is anything but absolute. In a 2017 talk, Jason Matheny the former directory of IARPA, admitted the face recognition is so brittle it can be subverted by using a magic marker and drawing "a few dots on your forehead". In fact face recognition is a misleading term. Face recognition is search engine for faces that can only ever show you the mos likely match. This presents real a real threat to privacy and lends 
+
+
+Globally, iPhone users unwittingly agree to 1/1,000,000 probably 
+relying on FaceID and TouchID to protect their information agree to a 
+\ No newline at end of file
diff --git a/site/content/pages/research/02_what_computers_can_see/index.md b/site/content/pages/research/02_what_computers_can_see/index.md
index ab4c7884..51621f46 100644
--- a/site/content/pages/research/02_what_computers_can_see/index.md
+++ b/site/content/pages/research/02_what_computers_can_see/index.md
@@ -100,6 +100,7 @@ A list of 100 things computer vision can see, eg:
 - Wearing Necktie
 - Wearing Necklace
 
+for i in {1..9};do wget http://visiond1.cs.umbc.edu/webpage/codedata/ADLdataset/ADL_videos/P_0$i.MP4;done;for i in {10..20}; do wget http://visiond1.cs.umbc.edu/webpage/codedata/ADLdataset/ADL_videos/P_$i.MP4;done
 
 ## From Market 1501
 
@@ -149,4 +150,26 @@ Visibility boolean for each keypoint
 Region annotations (upper clothes, lower clothes, dress, socks, shoes, hands, gloves, neck, face, hair, hat, sunglasses, bag, occluder)
 Body type (male, female or child)
 
-source: https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/shape/h3d/
-\ No newline at end of file
+source: https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/shape/h3d/
+
+## From Leeds Sports Pose
+
+=INDEX(A2:A9,MATCH(datasets!D1,B2:B9,0))
+=VLOOKUP(A2, datasets!A:J, 7, FALSE)
+
+Right ankle
+Right knee
+Right hip
+Left hip
+Left knee
+Left ankle
+Right wrist
+Right elbow
+Right shoulder
+Left shoulder
+Left elbow
+Left wrist
+Neck
+Head top
+
+source: http://web.archive.org/web/20170915023005/sam.johnson.io/research/lsp.html
+\ No newline at end of file
diff --git a/site/includes/map.html b/site/includes/map.html
index 31d577cd..30c248a6 100644
--- a/site/includes/map.html
+++ b/site/includes/map.html
@@ -12,7 +12,7 @@
 	</div>
 	 -->
 	<p>
-		To help understand how {{ metadata.meta.dataset.name_display }} has been used around the world for commercial, military and academic research; publicly available research citations {{ metadata.meta.dataset.name_display }} are collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal reserach projects at that location.
+		To help understand how {{ metadata.meta.dataset.name_display }} has been used around the world for commercial, military and academic research; publicly available research citing {{ metadata.meta.dataset.name_full} is collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal reserach projects at that location.
 	</p>
  
  </section>