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.
The O'Reilly Artificial Intelligence conference in New York is June 26-29, 2017. To train a machine learning system, you start with a lot of training data: millions of photos, for example. You divide that data into a training set and a test set. You use the training set to "train" the system so it can identify those images correctly. Then you use the test set to see how well the training works: how good is it at labeling a different set of images?
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.
We present a new corner finding algorithm based on merging like stroke segmentations together in order to eliminate false positive corners. We compare our system to two benchmark corner finders with substantial improvements in both polyline and complex fits. Sketch recognition is an emerging field that utilizes penbased interaction with computers. Handwriting recognition software in the modern operating systems allows users to write naturally, and applications have been created to recognize sketches in domains such as UML diagrams (Hammond & Davis 2002) and family trees (Alvarado & Davis 2004). In an attempt to perform free-sketch recognition, which allows users to draw as they would naturally without training or being trained by the system, certain geometric sketch recognition systems require a shape to be defined by a set of primitives (Hammond & Davis 2007).
Let's start with some comments about a recent ACLU blog in which they run a facial recognition trial. Using Rekognition, the ACLU built a face database using 25,000 publicly available arrest photos and then performed facial similarity searches of that database using public photos of all current members of Congress. They found 28 incorrect matches out of 535, using an 80% confidence level; this is a 5% misidentification (sometimes called'false positive') rate and a 95% accuracy rate. The ACLU has not published its data set, methodology, or results in detail, so we can only go on what they've publicly said. To illustrate the impact of confidence threshold on false positives, we ran a test where we created a face collection using a dataset of over 850,000 faces commonly used in academia.