Google claims its facial recognition system can achieve near 100 percent accuracy
Google published a paper detailing its new artificial intelligence system, FaceNet, which Google researchers call the most-accurate technology available for recognizing human faces, according to a report by Fortune.
In the paper, titled “FaceNet: A Unified Embedding for Face Recognition and Clustering”, Google claims the system achieved nearly 100-percent accuracy rate on the facial recognition dataset Labeled Faces in the Wild.
The dataset uses more than 13,000 face images from the Internet to measure how accurate the algorithms are at verifying whether two images are of the same individual.
FaceNet was trained on an enormous 260-million-image dataset and performed at an 86 percent and higher accuracy rate.
Last year, a team of Chinese researchers claimed to have achieved better than 99 percent accuracy.
In the June 2014 paper, Facebook researchers said that humans analyzing images in the Labeled Faces dataset are only able to achieve 97.5 percent accuracy.
However, Google researchers went beyond simply verifying whether two faces are identical by also attaching a name to a face.
Taking a classic facial recognition approach, the researchers went as far as organizing the images into groups of faces that look the most similar or the most distinct.
When Google’s FaceNet and Facebook’s “DeepFace” are eventually integrated into their company’s web platforms, they will automate the process of tagging photos and searching for people and will also make it easier for web companies to analyze their users’ social networks and to assess global trends.