TPG1 – Discussion
March 22nd, 2007 by eleanor - TPG1
Lego Hello World
I wish all my printers were made of legos.
LIFE photo archive hosted by Google
Images from Life Magazine going back to 1860’s, hosted by Google
Coming Face To Face With The President
Well crafted story about an under-heard point of view.
In California, Pot Is Now an Art Patron
A new funding source for the arts – reaping big rewards and funding many projects. It’s pot.
Notes on Portraiture in the Facebook Age
Celebrity Book Club: A List to End All Lists
Because, well, it’s sortof awesome.
Are "Artists' Statements" Really Necessary?
The pros and cons about that nemesis for most artists.
This to That
You tell it what you’ve got and it’ll tell you what to glue them together with.
Work of art: Online store for buyers, sellers
Not the TV show! Kelly Lynn Jones from Little Paper Planes is interviewed on her project, gives us a cheat sheet to local affordable art resources.
I saw this (fairly bad) movie the other night, Deja Vu, with Denzel Washington, where they used facial-recognition software on a backpack. art meets other art!
Check out this new commercial for a sony camera with facial recognition technology that finds the faces in a picture “because,” as a voice-over says, “the face makes the photo.” Its even got little green boxes and everything.
In defense of the algorithm, none of the photos were false-matches to my own face (there has been confusion about that)… simply erroneously detected as human. And actually, for three of the eight photos Ben & I stretched our definition of what counts as a match to a non-face (we included a match to a stencil of a human face, a match to a photo within the photo, and a match to a kitten). Also, keep in mind that there is an issue of scale here–the algorithm has so far evaluated 2,250,000+ photos–so even if an error is very unlikely, it will happen.
Finally, as explained by TPG, I did adjust the settings on the algorithm. When using facial-recognition software you have to balance the tendency false false-positives (i.e., not identifying a face that is there and/or not recognizing a match) vs. false positives (i.e., identifying a non-existent face and/or match). I turned the “knob” to err on the side of false positives.
wow, your face recognition algorithm must suck!
As part of the “Self-Portrait” project, Ethan “loosened” the parameters in order to produce more results.