The researchers report an initial round of experiments, using training data drawn from 800 mobile-device users.Those experiments suggest that about 10,000 training examples should be enough to lower the margin of error to a half-centimetre, which Khosla estimates will be good enough to make the system commercially viable..To collect their training examples, the researchers developed a simple application for smartphone devices.Scientists, including one of Indian-origin, have developed a new artificial intelligence software that can turn any smartphone into an eye-tracking device.Researchers built their eye tracker using machine learning, a technique in which computers learn to perform tasks by looking for patterns in large sets of training electric faucet Suppliers examples.

To get a sense of how larger training sets might improve performance, the researchers trained and retrained their system using different-sized subsets of their data.Researchers may enable new computer interfaces or help detect signs of incipient neurological disease or mental illness. We thought we should break this circle and try to make an eye tracker that works on a single mobile device, using just your front-facing camera," he said.The application flashes a small dot somewhere on the device's screen, attracting the user's attention, then briefly replaces it with either an "R" or an "L," instructing the user to tap either the right or left side of the screen. Previously, the largest data sets used to train experimental eye-tracking systems had topped out at about 50 users.

In addition to making existing applications of eye-tracking technology more accessible, the system developed by researchers at Massachusetts Institute of Technology (MIT) and University of Georgia may enable new computer interfaces or help detect signs of incipient neurological disease or mental illness.During this process, the device camera continuously captures images of the user's face. Researchers may enable new computer interfaces or help detect signs of incipient neurological disease or mental illness.They later acquired data on another 700 people, and the additional training data has reduced the margin of error to about a centimetre. To collect their training examples, the researchers developed a simple application for smartphone devices.On that basis, they were able to get the system's margin of error down to 1.

Their training set includes examples of gaze patterns from 1,500 mobile-device users, Khosla said.5 centimetres, a twofold improvement over previous experimental systems.The data set contains, on average, 1,600 images for each user."Since there are no applications, there's no incentive for people to buy the devices."Since few people have the external devices, there is no big incentive to develop applications for them," said Aditya Khosla, an MIT graduate student

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