Eyeball movement will be one of the methods for computer user identification [Full text]



Prof. Komgossee, Associate Professor at the University of Texas, Texas, proposed that human eyeball motion can be used as a computer user ID verification method

Traditional passwords are out of date. Scientists have successively proposed a series of new computer user authentication methods to verify e-mail and bank account passwords, finger waving, brain texture, palm texture, and even hip texture. These have become a new generation of password identification methods. At present, scientists propose eyeball movement as one of the methods for computer user identification.

Oleg Komorgosev, an associate professor of computer science at the University of Texas in the United States, believes that when a person looks at an object, his eyeball focuses on his fingerprint.

The eyeball has two types of movements, one called "beat", which is a rapid jerk behavior that usually occurs when people's eyeballs focus on an object, and the other is called "positioning", which usually occurs when the eyeball rests at a certain position. when. Normal human brain memory will remove the eyeball beat, thus achieving continuous images, but if you watch someone's eyes closely, you may see the eyeball beating.

Currently, Komorgosev designed a different type of stimuli - watching the dots on the screen and the test-type ink drops to track the eye movements of the testers. With sufficient data, the computer will create a personal eye movement image.

One of the problems is that this technology is still in its infancy. One of the problems is that there is a third of the error probability in the identification process, which is quite different from the expected one. But if combined with other types of eye biometric verification methods, such as retinal scans, it will analyze the blood pressure type of the retina or iris. In combination with Komorgasev's eye motion analysis method, the probability of error in scanning verification will drop to 5%, which will be very similar to the success rate of other recognition technologies.

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