Overview of the Last Article
Our last article dealt with what is deemed to be the Ultimate Biometric Modality of all-Retinal Recognition. By itself, the eye is a very powerful organ regarding both uniqueness and richness of data. There is often confusion between the two major parts of the eye, which are the Iris and the Retina.
The Iris is located in the front of the eye, and its primary function is to allow the proper amount of light into the eye structure to facilitate the line of vision. The Retina is located in the back of the eye, and it is a grouping of blood vessels which collects together at a central point, and from there, joins the front of the optic nerve. Its pri
mary purpose is to feed information into it, and from there, it is transmitted to the brain for further processing.
The Retina is a very stable component of the eye-it hardly ever changes over the lifetime of the individual, unless he or she is afflicted with some serious ailment, such as that of glaucoma. Since it is inside of the eye, it is also protected from the external environment, thus increasing its stability even more.
The Retina possesses many unique features -in fact, scientific studies have shown that it possesses up to 400 unique data points. Also, the Enrollment and Verification Templates which are created are very small in size at only 96 bytes. Thus, this allows for faster Verification and/or Identification transaction processing to take place.
But, despite all of the advantages of Retinal Recognition, it has suffered from one critical flaw-its user invasiveness. For example, this Biometric modality requires a great amount of cooperation from the end user, at a very close proximity. Because of this, it can be prone to high levels of error, especially in the area of the False Rejection Rate (the FRR).
As a result, it has very limited market application usage. It will only be deployed in those areas which require very high levels of security sophistication. Examples of this include nuclear facilities, military installations, and top secret research and development facilities. As such, it is expected that Retinal Recognition will not grow any more regarding market applications.
Introduction to Hand Geometry Recognition
On the other end of the spectrum of the Biometric modalities is that of Hand Geometry Recognition. In fact, it can be entitled as the “Oldest Biometric Technology of All”, because it even predates the evolution of Fingerprint Recognition.
The birth of Hand Geometry Recognition goes back all the way to the 1960s; it was patented in 1985, and by the early 1990s, it started to take heavy market dominance. At present, unlike the other Biometric modalities, there is only one primary vendor which develops this particular technology.
Also, in sharp contrast to the Retina, the hand itself does not really possess any unique data points. Rather, its distinctiveness comes from its shape. For this very reason, Hand Geometry Recognition is not really used in identification types of scenarios, where numerous data points are required. It is used primarily for Verification based applications, such as those of Physical Access Entry and Time/Attendance.
But, one of the key advantages of Hand Geometry Recognition is that it can be used in some of the harshest and most punishing environments imaginable.
For example, it can be used in warehouses, storage facilities, and factories, where the population sizes are very large. In fact, a single device can store over 40,000 Biometric templates of different individuals.
Hand Geometry Recognition can also be used in the most extreme of heat and cold, and has even been known to withstand such forces of nature as hurricanes and tsunamis.
The distinctiveness of the hand falls into seven categories:
- The overall shape of the hand;
- The various dimensions of the palm;
- The length and width of all ten fingers;
- The various measurements between the distances of the joints of the hand;
- The various shapes of the knuckles;
- The geometrical circumference of the fingers;
- Any distinct landmark features that can be found on the hand.
The Biometric Template Creation Process of Hand Geometry Recognition
To start the Enrollment Process with a Hand Geometry Recognition system, a combination of both prisms and Light Emitting Diodes (also known as LEDs) are used from within the scanner to capture the raw images of the hand.
All sides of the hand are taken into consideration, including the front, the back, and the palm. After these raw images are taken, a composite 3D picture of the hand is then created.
To capture the best raw images possible, five guiding pegs are located just beneath the camera to help guide the individual into properly positioning his or her hand.
A serious flaw with this process is that the images of these pegs are also captured. Thus, extra processing time is required to crop out them out from the raw images.
Because of this, the extraction algorithm cannot take into account any variances which can be caused by the rotation or different placements of the hand. To successfully create the Enrollment Template, the average measurements taken from the seven categories (as described in the previous section) are calculated.
These are then converted over into a binary mathematical file in a manner very similar to that of Fingerprint Recognition. In fact, the file itself is very small, only 9 Bytes.
Another flaw in creating both the Enrollment and the Verification Templates is that the geometric features of the hand share quite a bit of physiological resemblance with another. As a result, this can greatly hinder the unique feature extraction process.
To help alleviate this problem, a method known as “Principal Component Analysis” (also known as “PCA”) is utilized. This allows for a set of uncorrelated features to be “cloned” from the raw images so that the unique features can be extracted.
Hand Geometry Recognition also becomes the modality of choice for creating, deploying, and implementing a multi-layered security solution (it is best used for a Physical Access Entry application). It actually works very well even in a standalone mode, for both Physical Access Entry and Time/Attendance applications.
The Strengths and Weaknesses of Hand Geometry Recognition
Just like Retinal Recognition, the advantages and disadvantages of Hand Geometry Recognition can be classified according to the same seven criteria:
This is one of the biggest strengths of this particular modality. Most individuals have at least one hand that can be scanned for unique features, and the technology even has advanced to the point that even any physical deformities (up to a certain degree) can be taken into consideration. There are even Hand Geometry Scanners which are designed exclusively for left-handed individuals. The technology is very easy to use, and to train individuals on. All that is needed is the proper placement of the hand in the five pegs.
Although most individuals possess some unique features to their hands, it does not possess rich data, like the Retina.
This is probably one of the biggest disadvantages of Hand Geometry Recognition. Because the hand can be considered to be an external component of the human anatomy, it is prone to the harshness of the external environment. The shape of the hand can also be greatly influenced to a certain degree by weight loss and weight gain, injuries, and even other types of ailments (such as rheumatism).
The raw images which are captured of the hand are not affected by the surface elements of the skin. This includes grime, dirt, and scars.
Despite the lack of the richness of data, the accuracy rate of Hand Geometry Recognition is ranked very high. It has an Equal Error Rate (also known as the “ERR,” this is where the False Acceptance Rate and the False Rejection Rate equal each other). But, the device itself is very bulky, and this can prove to be a serious disadvantage regarding end-user perception and acceptance.
For the most part, Hand Geometry Recognition is very well accepted by the general population worldwide. This can be attributed to the fact that it is perceived to be noninvasive. The only negative aspect is the concern of the hygiene of the actual platen on which the hand is placed, as this requires direct contact by the end user.
Resistance to Circumvention:
Hand Geometry Recognition is very difficult to spoof because this requires creating an entire 3-D physical mockup of the hand.
A Case Study: Yeager Airport
Ever since the tragic events of 9/11, security at the major international airports and airlines has obviously become of great importance. For this very purpose, Biometrics has become one of the tools of choice to enhance the levels of security.
Although it is very important to screen passengers traveling through the airports, it is also equally important to screen the airport and airline employees as well. After all, they probably pose a greater threat to security because they know their way around through all of the access points. Because of this, even the smaller to medium sized airports across the United States have also started to implement Biometrics.
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A good example of this is Yeager Airport, located in Charlestown, West Virginia. It has a land space of about 740 acres, and six commercial airlines serve the airport and the surrounding communities. A primary reason for the increased need of security is that the control tower is located in the heart of the main passenger terminal.
Other security concerns included:
- The main entrance doors to the airport open an average of five times per hour, around the clock;
- The cost of maintaining a physical security presence was staggering – as much as $1,200 per day;
- The airport’s HVAC system, as well as other types of sensitive equipment, are also located in a basement near the stairwell which leads to the control tower.
The management team at Yeager Airport ultimately chose the “HandKey II” devices, manufactured by Schlage Recognition System. Five scanners were initially installed, with many more being planned for the future.
Once an airport employee has been properly verified and authenticated by the Hand Geometry Scanner, only then will the airport doors open, and entrance to the control tower be granted. Also, by implementing the use of Biometric Technology, the costs of having to hire a physical security presence is no longer needed, thus resulting in huge cost savings for the airport.
Apart from enhancing airport security, it is heavily expected that the use of Hand Geometry Recognition will grow quite rapidly in the Federal Government and Law Enforcement sectors. For example, although it is not currently being used in the e-Passport infrastructure at present, serious consideration is being given to it.
Also, like the Automated Fingerprint Identification System (AFIS) is being continually updated by the FBI, it is quite likely that Hand Geometry Recognition will also be used as a complement to Fingerprint Recognition.
Finally, although Hand Geometry Recognition is considered to be the “Oldest Biometric Technology,” it is by no means going to be replaced in the long term. For example, predictions are that the growth rate of this modality will continue at almost 23% in the coming years, up to 2019.