Feeling quite excited to see this coming together.

The project concept is the hijacking of identity data.... re-purposing it for art by stripping out the useful 'identity' part of the data and applying 'artistic' aesthetic to the results.

Lots of big question marks about what this means and what my strongest intentions are, but at the moment, just enjoying the journey into Big Data.

I was surprised to find out that for less than a 1000 Euros you can buy an SDK for facetracking that lets you monitor your employees facial expressions through webcameras.... Yep. Your boss knows how you feel, and can accumulate statistics about how many times you smiled today.



And here is a video posted by Adam Harvey (NY privacy advocate and digital artist) about how OpenCV tracking algorithms detect faces by analysing shapes and tones.


OpenCV Face Detection: Visualized from Adam Harvey on Vimeo.

This video visualizes the detection process of OpenCV's face detector. The algorithm uses the Viola Jones method of calculating the integral image and then performing some calculations on all the areas defined by the black and white rectangles to analyze the differences between the dark and light regions of a face. The sub-window (in red) is scanned across the image at various scales to detect if there is a potential face within the window. If not, it continues scanning. If it passes all stages in the cascade file, it is marked with a red rectangle. But this does not yet confirm a face. In the post-processing stage all the potential faces are checked for overlaps. Typically, 2 or 3 overlapping rectangles are required to confirm a face. Loner rectangles are rejected as false-positives.
This visualization was done as part of the documentation for CV Dazzle, camouflage from face detection. For more information, visit http://cvdazzle.com