article_snippet: In the paper, titled 'FaceNet: A Unified Embedding for Face Recognition and Clustering', Google claims System achieved nearly 100-percent accuracy rate on the facial recognition dataset Labeled Faces in the Wild.
article_snippet: the facial recognition dataset Labeled Faces in the Wild 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 . '
article_snippet: To train, Our use triplets of roughly aligned matching / non-matching face patches generated using a novel online triplet mining method. '
article_snippet: The benefit of our approach is much greater representational efficiency: we achieve state-of-the-art face recognition performance using only 128-bytes per face. '
article_snippet: On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%. '
article_snippet: FaceNet was trained on an enormous 260-million-image dataset and performed at an 86 percent and higher accuracy rate.
article_snippet: 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.
article_snippet: FaceNet was trained on an enormous 260-million-image dataset and performed at an 86 percent and higher accuracy rate.