In the previous article, Ravi Das explained how to evaluate a facial recognition system as well as the advantages and disadvantages of that technology. Next in his series on physical and behavioral biometric technologies, the author introduces the topic of retinal recognition technology by describing the physiology of the retina and some background about retinal scanners.
Before a discussion of retinal recognition technology can take place, it’s important to understand what the retina is and how it functions as part of the eye. Unlike the iris, the retina is a grouping of blood vessels which lead into the optic disc, and from there, visual information is transmitted to the brain for processing via the optic nerve, which lies in between the optic disc and the brain.
The scanning of the retina is known as retinal recognition, and at the present time, it is deemed to be the most unique biometric of all because of the richness of the data it possesses, from which the unique features can be extracted from.
Retinal recognition technology was first conceived in the mid 1970’s, but the first retinal recognition biometric device did not come out onto the marketplace until the early 1980’s. Although retinal recognition does possess some distinct advantages over the other biometric technologies, this technology has extremely limited market applications.
An image of the retina can be seen in Figure #1, below. An overall side view of the eye, which includes both the retina and the iris, can be seen in Figure #2.
Figure 1 Figure 2
The physiology of the retina
It is said that the retina “is to the eye as film is to a camera.” [Source 1]
The retina consists of multiple layers of sensory tissue and millions of photoreceptors whose function is to transform light rays into electrical impulses. These impulses subsequently travel to the brain via the optic nerve, where they are converted to images. Two distinct types of photoreceptors exist within the retina: the rods and the cones.
While the cones (of which each eye contains approximately 6 million) help us to see different colors, the rods (which number 125 million per eye) facilitate night and peripheral vision. It is the blood vessel pattern in the retina that forms the foundation for retinal recognition as a science and technology.
Because of its position within the eye, the retina is not exposed to the external environment. As a biometric, it is therefore very stable. It is from the retina that information is transmitted to, and received from, the brain. The circle in Figure 1 indicates the area that is typically captured by a retinal scanning device. It contains a unique pattern of blood vessels.
There are two famous studies that have confirmed the uniqueness of the blood vessel pattern found in the retina. The first was published by Dr. Carleton Simon and Dr. Isodore Goldstein in 1935 and describes how every retina contains a unique blood vessel pattern. In a later paper, they even suggest using photographs of these patterns as a means of identification.
The second study was conducted in the 1950s by Dr. Paul Tower. He discovered that, even among identical twins, the blood vessel patterns of the retina are unique and different.
Research, development, and manufacture of retinal scanners
The first company to become involved in the research, development and manufacture of retinal scanning devices was EyeDentify, Inc. The company was established in 1976 and its first retina capturing devices were known as ‘fundus cameras.’ While intended for use by ophthalmologists, modified versions of the camera were used to obtain retina images. The device had several shortcomings, however. First, the equipment was considered very expensive and difficult to operate. Second, the light used to illuminate the retina was considered too bright and too discomforting for the user.
Further research and development yielded the first true prototype scanning device, which was unveiled in 1981. The device used infrared light to illuminate the blood vessel pattern of the retina. The advantage of infrared light is that the blood vessel pattern in the retina can “absorb” such light much faster than other parts of the eye tissue.
The reflected light is subsequently captured by the scanning device for processing. In addition to a scanner, several algorithms were developed for the extraction of unique features. Further research and development gave birth to the first true retinal scanning device to reach the market: the EyeDentification System 7.5. The device utilized a complex system of scanning optics, mirrors, and targeting systems to capture the blood vessel pattern of the retina.
Subsequent development resulted in devices with much simpler designs. Later scanners consisted of integrated retinal scanning optics, which sharply reduced manufacturing costs (compared to the EyeDentification System 7.5).
The last retinal scanner to be manufactured by EyeDentify was the ICAM 2001, a device capable of storing up to 3,000 templates and 3,300 transactions. The product was eventually withdrawn from the market on account of its price as well as user concerns.
Ultimately, the primary reasons why retinal recognition scanners are not routinely used are the high cost of retinal recognition devices (which can be as high as $5,000 per device), their bulkiness, and the fact that the scanners are quite invasive for the end user. Although retinal recognition technology is a non-contactless technology, the end user must sit very close to the scanner, and literally place his or her eye onto the scanning device, thus making it very cumbersome and uncomfortable for the end user.
Up Next: Our next article will describe the process of retinal recognition, as well as its advantages and drawbacks.
Sources/References:
- “Retina Identification,” Robert “Buzz” Hill. Article is from the book Biometrics: Personal Identification in Networked Society by Anil Jain, Ruud Bolle, and Sharath Pankati ‑ p.124.
Ravi Das is a Cybersecurity Consultant and Business Development Specialist. He also does Cybersecurity Consulting through his private practice, RaviDas Tech, Inc. He also possesses the Certified in Cybersecurity (CC) cert from the ISC2.
Visit his website at mltechnologies.io
