article_snippet: Google claims Google facial recognistion 'FaceNet' system is practically perfect, getting the right person 99.96 per cent of the time.
article_snippet: 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 . '
article_snippet: Our method uses a deep convolutional network trained to directly optimize the embedding itself, rather than an intermediate bottleneck layer as in previous deep learning approaches,' the researchers say. '
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.