Last week, Guthrie Dolin and I presented at the Planning-ness 2012 conference. The conference was great, and I love it's theme, "Get Excited and Make Things." 

The standard format for a session is present on a topic and then run a workshop in which conference participants make something to present back to the group.

In our session we provide an overview of the Internet of Things before going into detail about the potential of connected objects to encourage specific behaviors. We break down the components of an IoT ecosystem and discuss how they can be used to collect data and drive insights and change.

Following the presentation, the assignment was to use the framework and design a physical product and service around a personal object or situation. We provided a few catalysts and blank worksheets to help get people going. The results were great and we've posted each group's worksheets on slideshare as well.

All-in-all, a great expreience and a ton of fun. Thanks to everyone who turned out to learn about the Internet of Things.

This year's eggs were designed using Nodebox and Inkscape.

Charlie worked mostly in Nodebox. It's patch-based programming was right at his level and copy, rotate and wiggle all got a lot of action.

Wyatt found inspiration in Minecraft and we worked together to write a letter writing script in Python to print out an wiki entry on egg. Then he created a vector version of the pixelated egg in Inkscape.

The video below shows some highlights from production.

Face.com offers a robust API for facial recognition. In short, you send it an image and it will process that image finding faces within it and return information about what it finds. That information can be used to locate features of the face as well as other characteristics of the face. These other characteristics include the age and gender as well as the presence of glasses or a smile. 

As a test, I built a standalone app that sends faces appearing in an attached web cam to the API for analysis. 

First, I built a basic Flash AIR application to upload images to the face.com API. For this I used Jean Nawratil's Flash ActionScript 3.0 Client Library and sent images each time a key was pressed on the keyboard. The only modification I made to Jean's library was to add a parameter to the POST made to the API. The parameter (attributes=all) tells the API to return additional characteristics.

With this in place I integrated OpenCV to locally detect when a face is present in the frame. This way, I could automatically send images to the API, but only those with something for face.com to find -- reducing the load on their servers (and my rate limit).

I've used an AS3 implementation of OpenCV in the past, but the performance was terrible. For this project, I used Wouter Verweirder's AIR Native Extension which offloads the work to native code. It is very responsive.

Using a standard face cascade, OpenCV recognizes faces and indicates approximately where they are in the frame. Based on this detection, my app sends a copy of the image to face.com for further analysis. The round trip to the face.com API is quick and generally takes less than a second, two at the most. I added a timer to the app to further limit how often I send off an image to the API.

Below are some captures from this basic proof of concept. Below each are the characteristics detected by the API.

In addition to basic face detection, the face.com API allows you to identify and tag people based on images you use to train the algorithm and/or images in a Facebook account. For more information about the API and what it can do, be sure to check out their online demo.

Where does my tweet go provides a clear visualization to see how a tweet spreads. The timeline and the vertical green line indicating the number of followers a tweet has are two nice touches.