There are other biometric technologies which are not so developed currently. However with new innovations some of them have a great chance of being used in par with the abovementioned biometric systems. They may include.
Hand Geometry
Hand geometry is a biometric that identifies users by the shape of their hands. Hand geometry readers measure a user's hand along many dimensions and compare those measurements to measurements stored in a file.
Viable hand geometry devices have been manufactured since the early 1980s, making hand geometry the first biometric to find widespread computerized use. It remains popular; common applications include access control and time-and-attendance operations.
Since hand geometry is not thought to be as unique as fingerprints or irises, fingerprinting and iris recognition remain the preferred technology for high-security applications. Hand geometry is very reliable when combined with other forms of identification, such as identification cards or personal identification numbers. In large populations, hand geometry is not suitable for so-called one-to-many applications, in which a user is identified from his biometric without any other identification.
Palm Vein Biometrics
The technology works by identifying the subcutaneous (beneath the skin) vein patterns in an individual's hand. When a user's hand is placed on a scanner, a near-infrared light maps the location of the veins. The red blood cells present in the veins absorb the rays and show up on the map as black lines, whereas the remaining hand structure shows up as white. After the vein template is extracted, it is compared with previously stored patterns and a match is made.
Some of the advantages that vein pattern recognition provides are:
* The vein patterns are unique to each individual. Apart from size, the pattern does not change over time. This feature makes it suitable for one-to-many matching, for which hand geometry and face recognition may not be suitable. Vein recognition technology has a False Rejection Rate (FRR) of 0.01% and a False Acceptance Rate (FAR) of 0.0001%, hence making it suitable for high-security applications.
* Veins are located underneath the skin surface and are not prone to external distortion the way fingerprints are. This reduces the high failure to enroll (FTE) rate caused by bad samples. Vein patterns are difficult to replicate because they lie under the skin surface. Fingerprints can be duplicated using gummy fingers. Additionally, some vein recognition models come with ‘liveness' detection that senses flow of blood in veins.
* User friendliness: This technology overcomes aversion to fingerprinting and related privacy concerns since its traditional association to criminal activity is non-existent. In countries such as Japan, where there is strong opposition to fingerprinting, vein recognition has become the biometric technology of choice. It is relatively quick as it takes less than 2 seconds to authenticate. Some noncontact models are more hygienic than fingerprint readers.
* Potential fusion with other biometric technologies: With the popularity of multimodal biometrics, vein recognition technology could be used in conjunction with hand or fingerprint biometrics. Vein recognition can provide one-to-many matching, and hand geometry can be used for one-to-one matching, thereby enhancing security.
Challenges
* Invasive: While it is less invasive than iris scanning, the fact that the technology studies the subcutaneous level can create apprehensions among people, giving them the psychological impression that it could be a painful process.
* Expensive: The technology is not cheap enough for mass deployment. Vein recognition units cost $2,000 to $4,000, whereas hand geometry readers and fingerprint scanners are priced at $1,200 and $500, respectively.
* Large Size: The presence of a CC camera makes the unit larger than a fingerprint reader. This prevents its adoption for the fast-growing mobile computing and logical access application markets. Unless the size is reduced, it may not be able to match silicon scanners in terms of versatility. Although Hitachi's finger-vein scanning unit is compact enough for PC access, it is not small enough to be integrated with notebooks computers or cell phones, the way silicon fingerprint sensors are.
* The technology is still untested because only vendors confirm the accuracy levels. The lack of interest from governments and standards agencies has not proved its capability yet. There are no mandates encouraging adoption either.
Facial Thermography
Facial thermography detects heat patterns created by the branching of blood vessels and emitted from the skin. These patterns, called thermograms, are highly distinctive. Even identical twins have different thermograms. Developed in the mid-1990s, thermography works much like facial recognition, except that an infrared camera is used to capture the images. The advantages of facial thermography over other biometric technologies are that it is not intrusive-no physical contact is required- every living person presents a usable image, and the image can be collected on the fly. Also, unlike visible light systems, infrared systems work accurately even in dim light or total darkness. Although identification systems using facial thermograms were undertaken in 1997, the effort was suspended because of the cost of manufacturing the system.
Keystroke Dynamics
Keystroke dynamics is part of a larger class of biometrics known as behavioral biometrics; their patterns are statistical in nature. It is a commonly held belief that behavioral biometrics are not as reliable as physical biometrics used for authentication such as fingerprints or retinal scans or DNA. The reality here is that behavioral biometrics use a confidence measurement instead of the traditional pass/fail measurements.
The benefit to keystroke dynamics (as well as other behavioral biometrics) is that acceptance and rejection can be adjusted by changing the acceptance threshold at the individual level. This allows for explicitly defined individual risk mitigation–something physical biometric technologies could never achieve.
Another benefit of keystroke dynamics: they can be captured continuously—not just at the start-up time—and may be adequately accurate to trigger an alarm to another system or person to come double-check the situation.
In some cases, a person at gun-point might be forced to get start-up access by entering a password or having a particular fingerprint, but then that person could be replaced by someone else at the keyboard who was taking over for some bad purpose. In other less dramatic cases, a doctor might violate business rules by sharing his password with his secretary, or by logging onto a medical system but then leaving the computer logged-in while someone else he knows about or doesn't know about uses the system. Keystroke dynamics is one way to detect such problems sufficiently reliably to be worth investigating, because even a 20% true-positive rate would send the word out that this type of behavior is being watched and caught.
Researchers are still a long way from being able to read a keylogger session from a public computer in a library or cafe somewhere and identify the person from the keystroke dynamics, but we may be in a position to confidently rule out certain people from being the author, who we are confident is "a left-handed person with small hands who doesn't write in English as their primary language.
Others
Researchers are investigating a biometric technology that can distinguish and measure body odor. This technology would use an odor-sensing instrument (an electronic "nose") to capture the volatile chemicals that skin pores all over the body emit to make up a person's smell. Although distinguishing one person from another by odor may eventually be feasible, the fact that personal habits such as the use of deodorants and perfumes, diet, and medication influence human body odor renders the development of this technology quite complex.
Blood pulse biometrics measure the blood pulse on a finger with infrared sensors. This technology is still experimental and has a high false match rate, making it impractical for personal identification.
The exact composition of all the skin elements is distinctive to each person. For example, skin layers differ in thickness, the interfaces between the layers have different undulations, pigmentation differs, collagen fibers and other proteins differ in density, and the capillary beds have distinct densities and locations beneath the skin. Skin pattern recognition technology measures the characteristic spectrum of an individual's skin. A light sensor illuminates a small patch of skin with a beam of visible and near-infrared light. The light is measured with a spectroscope after being scattered by the skin. The measurements are analyzed, and a distinct optical pattern can be extracted.
Nailbed identification technology is based on the distinct longitudinal, tongue-in-groove spatial arrangement of the epidermal structure directly beneath the fingernail. This structure is mimicked in the ridges on the outer surface of the nail. When an interferometer is used to detect phase changes in back-scattered light shone on the fingernail, the distinct dimensions of the nailbed can be reconstructed and a one-dimensional map can be generated.
Gait recognition, recognizing individuals by their distinctive walk, captures a sequence of images to derive and analyze motion characteristics. A person's gait can be hard to disguise because a person's musculature essentially limits the variation of motion, and measuring it requires no contact with the person. However, gait can be obscured or disguised if the individual, for example, is wearing loose fitting clothes. Preliminary results have confirmed its potential, but further development is necessary before its performance, limitations, and advantages can be fully assessed.
Ear shape recognition is still a research topic. It is based on the distinctive shape of each person's ears and the structure of the largely cartilaginous, projecting portion of the outer ear. Although ear biometrics appears to be promising, no commercial systems are available.
Body Salinity Identification is a technique being developed jointly by IBM and Massachussetts Institute of Technology (MIT) . The product called Personal Area Network (PAN) exploits the natural salinity of the body by passing a small current of nanoamp magnitude through the body. As salt is a good conductor of electricity, the passed current data can be recorded from which the salinity can be derived.
