This is a temporary page. HyperFace launches Jan 16. Sign up for launch notification here
- False-face computer vision camouflage patterns for Hyphen Labs’ NeuroSpeculative AfroFeminism at Sundance Film Festival 2017.
HyperFace is being developed for Hyphen Labs NeuroSpeculative AfroFeminism project at Sundance Film Festival and is a collaboration with Hyphen Labs members Ashley Baccus-Clark, Carmen Aguilar y Wedge, Ece Tankal, Nitzan Bartov, and JB Rubinovitz.
NeuroSpeculative AfroFeminism is a transmedia exploration of black women and the roles they play in technology, society and culture—including speculative products, immersive experiences and neurocognitive impact research. Using fashion, cosmetics and the economy of beauty as entry points, the project illuminates issues of privacy, transparency, identity and perception.
HyperFace is a new kind of camouflage that aims to reduce the confidence score of facial detection and recognition by providing false faces that distract computer vision algorithms. HyperFace development began in 2013 and was first presented at 33c3 in Hamburg, Germany on December 30th, 2016. HyperFace will launch as a textile print at Sundance Film Festival on January 16, 2017.
Together HyperFace and NeuroSpeculative AfroFeminism will explore an Afrocentric countersurveillance aesthetic.
For more information about NeuroSpeculative AfroFeminism visit nsaf.space
How Does HyperFace Work?
HyperFace works by providing maximally activated false faces based on ideal algorithmic representations of a human face. These maximal activations are targeted for specific algorithms. The prototype above is specific to OpenCV’s default frontalface profile. Other patterns target convolutional nueral networks and HoG/SVM detectors. The technical concept is an extension of earlier work on CV Dazzle. The difference between the two projects is that HyperFace aims to alter the surrounding area (ground) while CV Dazzle targets the facial area (figure). In camouflage, the objective is often to minimize the difference between figure and ground. HyperFace reduces the confidence score of the true face (figure) by redirecting more attention to the nearby false face regions (ground).
Conceptually, HyperFace recognizes that completely concealing a face to facial detection algorithms remains a technical and aesthetic challenge. Instead of seeking computer vision anonymity through minimizing the confidence score of a true face (i.e. CV Dazzle), HyperFace offers a higher confidence score for a nearby false face by exploiting a common algorithmic preference for the highest confidence facial region. In other words, if a computer vision algorithm is expecting a face, give it what it wants.
How Well Does This Work?
The patterns are still under development and are expected to change. Please check back towards the end of January for more information.
Please check back towards the end of January for product photos
If you’re interested in purchasing one of the first commercially available HyperFace textiles, please add yourself to my mailing list at Undisclosed.studio
- Designs subject to change
- Displayed patterns are prototypes and are currently undergoing testing.
- First prototype designed for OpenCV Haarcascade. Future iterations will include patterns for HoG/SVM and CNN detectors
- Will not make you invisible
- Please credit image as HyperFace Prototype by “Adam Harvey / ahprojects.com”
- Please credit scarf rendering prototype as “Rendering by Ece Tankal / hyphen-labs.com”
- Not affilliated with the IARPA funded Hyperface algorithm for pose and gender recognition