A biometric device is a security identification and authentication device. Such devices use automated methods of verifying or recognising the identity of a living person based on a physiological or behavioral characteristic. These characteristics include fingerprints, facial images, iris and voice recognition.[1]

What are the 5 main types of biometrics?

While there are many types of biometrics for authentication, the five most common types of biometric identifiers are: fingerprints, facial, voice, iris, and palm or finger vein patterns.

Biometric devices have been in use for thousands of years. Non-automated biometric devices have in use since 500 BC,[2] when ancient Babylonians would sign their business transactions by pressing their fingertips into clay tablets.

Automation in biometric devices was first seen in the 1960s.[3] The Federal Bureau of Investigation (FBI) in the 1960s, introduced the Indentimat, which started checking for fingerprints to maintain criminal records. The first systems measured the shape of the hand and the length of the fingers. Although discontinued in the 1980s, the system set a precedent for future Biometric Devices.

Types of biometric devices

There are two categories of biometric devices,

  1. Contact Devices – These types of devices need contact of body part of live persons. They are mainly fingerprint scanners, either single fingerprint, dual fingerprint or slap (4+4+2) fingerprint scanners, and hand geometry scanners.
  2. Contactless Devices – These devices don’t need any type of contact. The main examples of these are face, iris, retina and palm vein scanners and voice identification devices.

The characteristic of the human body is used to access information by the users. According to these characteristics, the sub-divided groups are

  • Chemical biometric devices: Analyses the segments of the DNA to grant access to the users.
  • Visual biometric devices: Analyses the visual features of the humans to grant access which includes iris recognition, face recognition, Finger recognition, and Retina Recognition.
  • Behavioral biometric devices: Analyses the Walking Ability and Signatures (velocity of sign, width of sign, pressure of sign) distinct to every human.
  • Olfactory biometric devices: Analyses the odor to distinguish between varied users.
  • Auditory biometric devices: Analyses the voice to determine the identity of a speaker for accessing control.

Future

Researchers are targeting the drawbacks of present-day biometric devices and developing to reduce problems like biometric spoofing and inaccurate intake of data. Technologies which are being developed are-

  • The United States Military Academy are developing an algorithm[13] that allows identification through the ways each individual interacts with their own computers; this algorithm considers unique traits like typing speed, rhythm of writing and common spelling mistakes. This data allows the algorithm to create a unique profile for each user by combining their multiple behavioral and stylometric information. This can be very difficult to replicate collectively.
  • A recent innovation by Kenneth Okereafor[14] and,[15] presented an optimized and secure design of applying biometric liveness detection technique using a trait randomization approach. This novel concept potentially opens up new ways of mitigating biometric spoofing more accurately, and making impostor predictions intractable or very difficult in future biometric devices. A simulation of Kenneth Okereafor’s biometric liveness detection algorithm using a 3D multi-biometric framework consisting of 15 liveness parameters from facial print, finger print and iris pattern traits resulted in a system efficiency of the 99.2% over a cardinality of 125 distinct randomization combinations. The uniqueness of Okereafor’s innovation lies in the application of uncorrelated biometric trait parameters including intrinsic and involuntary biomedical properties from eye blinking pattern, pulse oxymetry, finger spectroscopy, electrocardiogram, perspiration, etc.
  • A group of Japanese Researchers have created a system[16] which uses 400 sensors in a chair to identify the contours and unique pressure points of a person. This derrière authenticator, still undergoing massive improvements and modifications, is claimed to be 98% accurate and is seen to have application in anti theft device mechanisms in cars.
  • Inventor Lawrence F. Glaser has developed and patented technology which appears at first to be a high definition display. However, unlike displays with 2 dimensional pixel arrays, this technology incorporates pixel stacks, accomplishing a series of goals leading to the capture of a multi-biometric. It is believed to be the first man-made device which can capture 2 or more distinct biometrics from the same region of pixel stacks (forming a surface) at the same instant, allowing for the data to form a third biometric, which is a more complex pattern inclusive as to how the data aligns. An example would be to capture the finger print and the capillary pattern at precisely the same moment. Other opportunities exist with this technology, such as to capture kirlean data which assures the finger was alive during an event, or capture of bone details forming another biometric used with the others previously mentioned. The concept of stacking pixels to achieve increased functionality from less surface area is combined with the ability to emit any color from a single pixel, eliminating the need for RGB (RED GREEN BLUE) surface emissions. Lastly, the technology was tested with high power cadmium magnetics to check for distortion or other anomalies, as the inventor wanted to also embed magnetic emission and magnetic collection with this same surface technology, but without exhibiting any magnetic stripes on the surface. Devices, such as smart cards, can pass magnetic data from any orientation by automatically sensing what the user has done, and using data about where the card is when “swiped” or inserted into a reader. This technology can detect touch or read gestures at distance, without a user side camera and with no active electronics on its surface. The use of Multibiometrics hardens automated identity acquisition by a factor of 800,000,000 and will prove to be very difficult to hack or emulate.