Facial recognition software used by the UK's biggest police force has returned false positives in more than 98 per cent of alerts generated, The Independent can reveal, with the country's biometrics regulator calling it "not yet fit for use". The Metropolitan Police's system has produced 104 alerts of which only two were later confirmed to be positive matches, a freedom of information request showed. In its response the force said it did not consider the inaccurate matches "false positives" because alerts were checked a second time after they occurred. Facial recognition technology scans people in a video feed and compares their images to pictures stored in a reference library or watch list. It has been used at large events like the Notting Hill Carnival and a Six Nations Rugby match.
We present a neural network-based face detection system. A retinally connected neural network examines small windows of an image, and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We use a bootstrap algorithm for training, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually selecting non-face training examples, which must be chosen to span the entire space of non-face images.
With the development of deep learning, Deep Metric Learning (DML) has achieved great improvements in face recognition. Specifically, the widely used softmax loss in the training process often bring large intra-class variations, and feature normalization is only exploited in the testing process to compute the pair similarities. To bridge the gap, we impose the intra-class cosine similarity between the features and weight vectors in softmax loss larger than a margin in the training step, and extend it from four aspects. First, we explore the effect of a hard sample mining strategy. To alleviate the human labor of adjusting the margin hyper-parameter, a self-adaptive margin updating strategy is proposed. Then, a normalized version is given to take full advantage of the cosine similarity constraint. Furthermore, we enhance the former constraint to force the intra-class cosine similarity larger than the mean inter-class cosine similarity with a margin in the exponential feature projection space. Extensive experiments on Labeled Face in the Wild (LFW), Youtube Faces (YTF) and IARPA Janus Benchmark A (IJB-A) datasets demonstrate that the proposed methods outperform the mainstream DML methods and approach the state-of-the-art performance.
Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. Unlike Beyoncé, we do not all wake up flawless--at least not according to the iPhone X. Several iPhone X–owning Twitter users have taken to the latter (probably using the former) to complain that Face ID--the phone's facial recognition technology--fails to recognize their face first thing in the morning. Like a drunken one-night stand, the iPhone X doesn't quite know who they are in the morning light. Face ID, Apple's follow-up to Touch ID, allows users to unlock their phone with their face--or more specifically, with a mathematical representation of their facial structure.
For the last few years, police forces around China have invested heavily to build the world's largest video surveillance and facial recognition system, incorporating more than 170 million cameras so far. In a December test of the dragnet in Guiyang, a city of 4.3 million people in southwest China, a BBC reporter was flagged for arrest within seven minutes of police adding his headshot to a facial recognition database. And in the southeast city of Nanchang, Chinese police say that last month they arrested a suspect wanted for "economic crimes" after a facial recognition system spotted him at a pop concert amidst 60,000 other attendees. These types of stories, combined with reports that computer vision recognizes some types of images more accurately than humans, makes it seem like the Panopticon has officially arrived. In the US alone, 117 million Americans, or roughly one in two US adults, have their picture in a law enforcement facial-recognition database.