Biometric device

Biometric devices have been familiar to humans for a long period of time. Manual biometric devices have been used since 500 BC. e. ^[2] , as seen on Babylonian clay tablets with business records and fingerprints. Automation in biometric devices first appeared in the 1960s, ^[3] when the FBI introduced Indentimat, a fingerprint verification device for maintaining a criminal record database. The first devices measured the shape of the hand and the length of the fingers. Although the system was minimized in the 1980s, it set a precedent for future biometric devices.

For user access, features of the human body are used. Based on them, the following biometric devices are distinguished:

• Chemical: analyze DNA segments.
• Visual: IRIS recognition, face recognition, finger recognition and retina recognition.
• Behavioral: analyze the speed, width of handwriting and pen pressure, individual for each person.
• Olfactory: discern odors from different users.
• Auditory: analyze the voice.

Jobs

With the increase in “Buddy Punching” ^[4] (when employees cover up the absence of comrades during working hours), employers turned to fingerprint recognition technology. Biometric devices also provide reliable ways to collect employee hours of work, as each one has unique biometric data.

Immigration

As the demand for air travel grows and the number of people increases, modern airports are forced to introduce technologies to reduce the number of long lines. Biometrics is being introduced in an increasing number of airports, because this system allows you to quickly recognize passengers. One such example is Dubai International Airport, which plans to introduce IRIS on the move (IOM) technology, which should facilitate the smooth departure and arrival of passengers at the airport. ^[five]

Handheld Devices

Fingerprint sensors can also be found on mobile devices. This sensor is used to unlock the device and authorize actions such as, for example, transferring money and files. It can be used to prevent unauthorized use of the device.

Personal Signature System

This is one of the most recognized ^[6] and acceptable biometric characteristics in a corporate environment. This system takes into account many parameters, for example, the pressure exerted when touching, the speed of the hand and the angle between the surface and the pen used for signature. It is also possible to learn from users, since signature styles for the same person differ. Therefore, taking a sample of the data, this system is able to increase its own accuracy.

Iris Recognition System

This system uses a device that scans the retina of the user's eye and then compares the result with those stored in the database. This is one of the most reliable forms of authentication, as fingerprints can be left on any surface, and iris prints are extremely difficult to steal. Iris Recognition is widely used by organizations working with large flows of people. One such system is the Aadhar identification, carried out by the Indian government to record the population. The choice of such a system is justified by the fact that the iris of the eye practically does not evolve during life.

Biometric Spoofing

Biometric spoofing is a method of deception ^[7] of a biometric identification control system in which fake material is given to a biometric scanner. This material imitates the unique biometric characteristics of a person in order to confuse the system and gain access to sensitive data.

One of such high-profile cases of falsification of biometric data was related to the fact that the fingerprint of German Defense Minister Ursula von der Leyen was successfully reproduced ^[8] by the Chaos Computer Club Group. The group used high-quality lenses and took pictures from a distance of 6 feet. They used professional finger software and mapped the contours of the fingerprint of the minister. However, there is a method of countering spoofing. Using the principle of pulse oximetry ^[9] , that is, measuring blood oxygenation and heart rate, an additional level of protection can be introduced. This reduces the number of attacks like the one mentioned above, although this method is not commercially feasible due to the high costs.

Accuracy

Accuracy is a serious problem in biometric recognition. Passwords are still extremely popular because the password is static in nature, while biometric data can be changed (voice becomes heavier due to puberty, the appearance of scars on the face can lead to an incorrect scan of the face). When testing voice recognition as a replacement for PIN-based systems, Barclays reported ^[10] that their voice recognition system is 95 percent accurate. This statistic means that many customer voices may not be recognized, even if they are true. This uncertainty may lead to slower implementation of biometric devices.

• Biometric data is unique for each person, and its hacking is complicated ^[11] , which makes devices of this type more secure than traditional authentication methods, since passwords can be easily stolen or forgotten. A study conducted among Yahoo users showed that at least 1.5 percent ^{[12] of} users forget their passwords every month, so access to the services of the service becomes longer for consumers, because the password recovery process takes a lot of time. These shortcomings of traditional passwords make biometric devices more efficient and reduce the efforts of the end user.

Researchers are working to study the shortcomings of modern biometric devices and are developing new ones in which the likelihood of falsification or data distortion is reduced. Technologies under development:

• The US Military Academy is developing an algorithm ^[13] that allows you to identify how each person interacts with their computers; This algorithm takes into account unique features, such as typing speed, writing rhythm and typical spelling errors. This data allows the algorithm to create a unique profile for each user, combining their numerous behavioral and stylometric data.
• A recent innovation by Kenneth Okereafor ^[14] and ^[15] presented an optimized and reliable way to apply the method of biometric determination of survivability using the method of randomization of signs. This new concept potentially opens up new ways to more accurately determine biometric spoofing and makes impostor authorization almost impossible in future biometric devices. The modeling of Kenneth Okereafor’s biometric algorithm for determining the vitality using a three-dimensional multibiometric structure consisting of 15 survivability parameters based on the characteristics of a face print, fingerprint, and iris resulted in a system efficiency of 99.2% for 125 different randomization combinations. The uniqueness of Okereafor's innovations lies in the application of uncorrelated biometric parameters, including internal and involuntary biomedical parameters, such as blinking of the eyes, pulse oximetry, finger spectroscopy, electrocardiogram, sweating, etc.
• A group of Japanese researchers created a system ^[16] that uses 400 sensors in a chair to determine the contours and unique pressure points created by humans. It is claimed that this derrière authenticator, which continues to improve and modify, has a accuracy of 98% and is used in the mechanisms of anti-theft devices for automobiles.
• Inventor Lawrence F. Glaser developed and patented a technology that at first glance looks like a high-definition display. However, unlike displays with two-dimensional pixel arrays, this technology includes pixel stacks that fulfill a number of goals and lead to a multi-biometric image. It is believed that this is the first artificial device that can capture 2 or more different biometric data from the same area of ​​pixel stacks (forming a surface) at the same time, allowing the data to generate third biometric information, which is a more complex sample with respect to of how the input is aligned. An example would be capturing a fingerprint and capillary pattern at the same time. There are other possibilities with this technology, such as Kirlian data collection, which ensures that the finger moves during an event, or capture bone parts that form other biometric information. The concept of stacking pixels to achieve enhanced functionality due to its smaller surface area is combined with the ability to emit any color from a single pixel, eliminating the need for RGB surface emission (RED GREEN BLUE). ^[17]