Facial detection software is on the verge of being implemented for business use: it can already detect average age, male or female, and even attention level. This software can be very valuable to marketeers and businesses in general. On the other hand, it is quite intrusive software and people might be worried about their privacy as it’s hard to keep your anonymity. One of existing face recognition software is NotiFace II, a face recognition surveillance system developed by Face-Tek Technology. It integrates face recognition with security monitoring products, as presented on the video.
Other examples of currently being developed facial detection software are:
- -SceneTap: a new application for smart phones that uses cameras with facial detection software to scout bar scenes which posts information like the average age of a crowd and the ratio of men to women, helping bar-hoppers decide where to go.
- -software for digital billboards developed by Immersive Labs: a face recognition software that uses cameras to gauge the age range, sex and attention level of a passer-by to deliver ads based on consumers’ demographics.
- -photo-tagging suggestion tool developed on Facebook: when a person uploads photos to the site, the “Tag Suggestions” feature uses facial recognition to identify that user’s friends in those photos and automatically suggests name tags for them.
- Picasa: a photo editing software from Google.
- -PhotoTagger: an application accessible on face recognition platform Face.com.
On the other hand, from academic point of view, researchers from Carnegie Mellon University also raised questions about the future of privacy in a world using large-scale automated identification combining online and offline data. They based their Face Recognition study on three experiments combining facial recognition software and publicly available photos.
Researchers leaded by professor Alessandro Acquisti demonstrated the easiness of identifying strangers online (which protect their identities by using pseudonyms), offline (on photos available on a social network site), and the ability of inferring strangers’ personal or sensitive information from their faces (by combining face recognition, data mining algorithms, and statistical re-identification techniques).
On the video below you can watch professor Alessandro Acquisti presenting his lecture “Privacy in the Age of Augmented Reality” at the 8th Annual CyLab Partners Conference, an annual event that allows attendees to immerse themselves in numerous cybernetic research projects.
