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Google: Our New System For Recognizing Faces Is the Best
schwit1 writes Last week, a trio of Google researchers published a paper on a new artificial intelligence system dubbed FaceNet that it claims represents the most accurate approach yet to recognizing human faces. FaceNet achieved nearly 100-percent accuracy on a popular facial-recognition dataset called Labeled Faces in the Wild, which includes more than 13,000 pictures of faces from across the web. Trained on a massive 260-million-image dataset, FaceNet performed with better than 86 percent accuracy.
The approach Google's researchers took goes beyond simply verifying whether two faces are the same. Its system can also put a name to a face—classic facial recognition—and even present collections of faces that look the most similar or the most distinct. Every advance in facial recognition makes me think of Paul Theroux's dystopian Ozone.
The approach Google's researchers took goes beyond simply verifying whether two faces are the same. Its system can also put a name to a face—classic facial recognition—and even present collections of faces that look the most similar or the most distinct. Every advance in facial recognition makes me think of Paul Theroux's dystopian Ozone.
Different databases used for different tests.
13,000 pictures used in Labeled Faces in the Wild test
260M pictures used in another test
100-percent accuracy and recognizing the presence of a face in a photo
86-percent accuracy in determining the identity of the face in the photo
Scientists and engineers are by definition not supposed to be ethical.
Professional Engineers (PEs) disagree:
Ethics - National Society of Professional Engineers
and
National Society of Professional Engineers (NSPE) Code of Ethics for Engineers