You’ve probably heard before that focal lengths between 85mm and 135mm produce the best head shots because they provide a desirable perspective in head shots, but how much of a different does the focal length actually make? Photographer Stephen Eastwood decided to find out, shooting 10 portraits of the same subject with focal lengths ranging from 19mm to 350mm.
Lens Distortion (via Orms Connect)
Image credits: Photographs by Stephen Eastwood
Inspired by Noah Kalina’s viral everyday video a girl who goes by clickflashwhirr has been doing a similar self-portrait-a-day project. Designer Tiemen Rapati decided to make a composite image showing what the average of the self-portraits looks like. Taking 500 images from clickflashwhirr’s Flickr set, Rapati wrote a script that counts the individual RGB values for each pixel, averaging them across the 500 portraits.
PillowMob is a new service that transforms photos of heads into puffy pillow heads. In addition to human faces, you can also use the face of your beloved pet. They cost $25 each with free shipping — it’s currently available to US residents only, but the company may begin shipping internationally soon.
Thanks for the tip, Jeremy!
It’s finally happened — companies are starting to realize that the two lenses on 3D cameras look a whole lot like eyes. This 3-megapixel “Felyne” camera is designed to look like a character from the video game franchise Monster Hunter, and goes on sale later this month in Japan for about $90. Something tells me we’ll be seeing a lot more of this kind of thing if 3D cameras start becoming popular.
I wonder if camera makers can make these things look like they’re blinking whenever you take a picture. That’d be neat… or creepy.
(via Famitsu via PhotoWeeklyOnline)
Looks like Facebook’s recent acquisition of Divvyshot was not for naught.
In a post on the Facebook blog yesterday, Divvyshot founder Sam Odio announced that Facebook is adding the same face detection features found in many consumer cameras to its uber-popular Facebook Photos app.
Previously, users had to manually select each face found in a photograph to tag it with a friend’s name, but now the service will automatically select each face and prompt you for the name, streamlining the process and making it much easier for uploaders.
I’m guessing we’ll soon see features added that promote collaboration and pooling together photographs as a group to a shared pool, similar to what Divvyshot offered prior to the acquisition and shutdown.
Here’s some nerdy news: Israeli facial recognition startup Face.com has just opened up its API, allowing developers to integrate its facial recognition technology in third-party websites and applications. Since launching a year ago, the company has scanned more than 7 billion photos and tagged more than 52 million faces through its Photo Finder and Photo Tagger applications on Facebook.
Now, the technology is no longer limited to Facebook, as any third-party developer can integrate facial recognition into their own apps. The API uses a REST-like interface similar to Twitter’s API, and takes in URLs to photos.
I’m interested in seeing what kind of creative applications developers can come up with. They’ve post a few example apps already using the API, and there’s talk of upcoming facial recognition augmented reality apps.