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SPOKEN BY: Annie Gaus - 12-05-2012

HOW ACCURATE IS FACIAL RECOGNITION & FACEBOOK'S APP CENTRE
APPJUDGEMENT / EPISODE 436 / 12-05-2012

This week we look at Klik, the new photo-sharing app that recognizes your friends' faces automatically. However, how accurate is it? In addition, Facebook announces a new way to annoy you... err, help you discover apps.

JOSEPH LYNCH:
Klik information can be found at [ Ссылка ] .

HOW FACE RECOGNITION WORKS
Face.com's face recognition is a service, which allows computers to analyse facial information found in photos, and attempt to identify faces against a known set of users. We target the hardest problem in this field: identifying 'faces in the crowd', appearing in everyday photos, and match them against a large set of known faces. The technology we're offering through our APIs scales to many users, and was built with consumer applications in mind: fun, productivity and privacy.

The process of face recognition requires an index of known faces before recognition can be performed. This process is also called 'training', and it works by adding face tags of known users to the index (or training-set), and then processing these photos to create a new index entry. More saved tags per user means higher accuracy. Once you have your users all set up in the index, you can search for them in photos.

The face.com index supports users of known social networks as well as private namespaces. We currently support Facebook and Twitter user indexing, which are shared across all API users, and include data access and privacy enforcements using valid user credentials. With Facebook and Twitter user IDs, only access to friends of the currently logged-in user is permitted. Private Namespaces are suitable for building an application-specific index of faces that do not necessarily have existing profiles on Facebook or Twitter, and are not be shared with other users of the APIs. Training and enforcement of privacy policies in private namespaces is entirely up to the developer, and must correspond to our Terms.

TIPS FOR IMPROVING FACE RECOGNITION
The quality of recognition results is influenced by the index quality, the search input and interpreting the results in a manner most suitable for your service:
•Training Set - a single face tag is sufficient to support training and recognition, but the more face tags a user has, the better accuracy gets for that user. We recommend you use 10 or more tags for best accuracy, especially if recognizing against a large set of users (1000+ friends). Use tags.save and faces.train regularly, possibly as part of a tagging or confirmation UX of your service.
•Number of UIDs per call - although the percentage of false positives remains the same, the actual number of mistakes is expected to increase as you increase the number of UIDs per search. Where possible, passing higher relevancy users will or the *user@namespace notation can result in higher accuracy and faster response times.
•Confidence level - when performing recognitions, you get multiple results per detected face and may select either the top selection or multiple choices. With each detected face tag, we also provide a threshold score, which can be used to screen out low-confidence scores. Note that the confidence threshold we provide is specific to the call you made, and may change if, for instance, you change the number of UIDs in your search.
•Photo Quality - face.com can handle any JPG photo, and was built to handle web resolutions and everyday conditions. That said, extremely low resolution, lighting, smearing, focus, and other issues may result in lower accuracy.


POST BY:
Joseph Lynch
Business Development Manager,
Network Engineer & Social Marketing Manager
JR Accounting Compass
Sydney, Australia
Information Technology and Services
[ Ссылка ]




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