Identity theft is increasingly a 21st-Century problem. As more data moves off of physical paper and onto Internet-connected servers, the chances of that data getting stolen increases as well.
According to the FTC, there were 2.8 million fraud reports from consumers in 2021, a nearly 27% increase over the 2.2 million fraud reports in 2020. 26.4% of these were from people between the ages of 30 and 39, while just 4.7% were from people over 70 years old. People who use Facebook, Instagram, and Snapchat were particularly vulnerable. Users on these sites have a 46 percent higher risk of account takeovers and fraud than those not active on any social media networks.
Norton found that 87 percent of consumers have left their personal information exposed while accessing emails, bank accounts or financial information, another issue that could be mitigated through the use of a VPN.
Identity fraud is increasingly committed by sophisticated criminal organizations operating beyond the reach of outdated laws that do not address such crimes. A proliferation of personally identifiable information (PII) available through social media and other public sources is easily accessible to aspiring fraudsters, while the anonymity of Internet commerce and communication gives them plenty of cover. identity fraud and its derivative crimes cost banks, retailers, healthcare providers, governments, and ultimately consumers and taxpayers around the globe hundreds of billions of dollars every year, and this figure continues to grow.
Biometrics are rapidly making their way into the mainstream as a means to help prevent identity theft and fraud. Biometric verification is any means by which a person can be uniquely identified by evaluating one or more distinguishing biological traits. There are two categories of biometric identification and recognition solutions: Physical and behavioral. Physical biometric solutions use distinctive and measurable characteristics of particular parts of the human body, such as a person’s face, iris, DNA, vein, fingerprints, etc., and transform this information into a code understandable by the AI system. Behavioral biometric solutions operate in a similar way, except they use unique behavioral characteristics, such as a person’s typing rhythm, way of interaction with devices, gait, voice, etc.
Biometric solutions are typically used for security and access control across businesses and government organizations. Fingerprint recognition is one of the oldest, simple to install, and low-cost technology; therefore, it finds numerous applications and is widely adopted by many industries. In travel and immigration, fingerprint recognition technology is used in e-passports, e-visas, and driving licenses to authenticate an individual.
In the consumer electronics industry, fingerprint recognition technology is used in laptops, computers, and smartphones, among others. Apple “Face ID” feature can unlock your iPhone X with your face. The biometrics has now merged with other characteristics of physical body , for example Iris-pattern and retina-pattern authentication methods are already employed in some bank automatic teller machines.
Facial recognition supporters in the US often argue that the surveillance technology is reserved for the greatest risks — to help deal with violent crimes, terrorist threats and human trafficking. On the other hand, China’s facial recognition system logs nearly every single citizen in the country, with a vast network of cameras across the country. A database leak in 2019 gave a glimpse of how pervasive China’s surveillance tools are — with more than 6.8 million records from a single day, taken from cameras positioned around hotels, parks, tourism spots and mosques, logging details on people as young as 9 days old. China is racing ahead in its use of facial recognition technology, despite widespread concerns about its impact on privacy and civil liberties.
In this complex human terrain, biometric technologies helped put a uniform on the nation’s enemies and reduced their ability to leverage anonymity for military advantage. Military and Security require more technologically advanced methods of ensuring security against terrorist activities and Illegal immigration. One of the most effective methods of curbing the same is by creating biometric authentication across borders and airports. “Biometric identification (perhaps at range) may strip away the anonymity that enables insurgents to blend into a society –or will allow future adversaries to identify, track, isolate, and target individual U.S. political or military leaders,” writes DOD report.
In a nutshell, biometric systems are based on three pillars:
- Sensors: subcomponents that detect and measure biometric data and digitize it.
- Templates: signal processing algorithms and techniques create biometric templates of the user. Later, these templates are compared to stored data and matched with existing profiles.
- Decision Rules: a decision process that uses matching event results.
Biometric verification has advanced considerably with the advent of computerized databases and the digitization of analog data, allowing for almost instantaneous personal identification. This encoded biometric information is stored in a database and digitally sampled during authentication and verification. A record of a person’s unique characteristic is captured and kept in a database. Later on, when identification verification is required, a new record is captured and compared with the previous record in the database. If the software matches the data in the new record with that in the database record, the person’s identity is confirmed, it then grants the appropriate level of access.
For more details please visit: Securely Identifying You: A Comprehensive Guide to Biometrics and Security
Biometric Authentication Through Physical Characteristics
Characteristics like fingerprints, face recognition, hand geometry, voice recognition, palm vein recognition, retina scans, iris recognition, and signature verification are the most common types of characteristics used for biometric authentication.
Signature comparison is not as reliable, all by itself, as the other biometric verification methods but offers an extra layer of verification when used in conjunction with one or more other methods.
An ideal security solution utilizes multiple biometrics as part of a multi-factor authentication system. The advantage of multi-factor authentication is that it is incredibly unlikely that an unauthorized user will be able to obtain all the necessary credentials needed to gain access. This is especially true when it comes to biometric data, which is incredibly difficult to forge due to its highly complex nature.
Through optical/ultrasound/thermal sensors, fingerprints can be digitally collected and stored. Fingerprints are considered the most popular biometric identification method and have been in extensive use for more than a century. Most fingerprint biometric solutions look for specific features of a fingerprint, such as the ridge line patterns on the finger, the valleys between the ridges, etc., commonly known as minutiae, which are then converted to stored digital data. The most widely used technique, minutiae-based matching, compares the location and direction of minutiae points. Each fingerprint contains 30 to 40 minutiae points and no two people have more than eight points in common.
A facial recognition biometric system identifies and verifies a person by extracting and comparing selected facial features from a digital image or a video frame to a face database. Using statistical patterns, facial recognition measures different points on an individual’s face to extract data and match them to pre-existing templates associated with the individual to verify his/her identity.
Facial-recognition technology has been used by law enforcement to pick out individuals in large crowds with considerable reliability. Facial recognition is going to play an increasing role, especially in surveillance and border security applications, due to increasing concerns about terrorism and mass migration
These systems use special sensors to identify the iris and map segments into vectors, which include spatial and orientation data. This data is converted to a unique code and compares it to other stored codes.
Technavio defense research analyst Moutushi Saha asserts in a report summary that the “US military has been using iris scan technology for over a decade in Iraq and Afghanistan to authorize selected individual’s entry into the military facilities in the US bases.” Such access control applications will continue to be important, but iris scanning is also finding its way into other areas such as passport control and civil ID, as seen in India’s Aadhaar program.
Speaker or voice recognition
Speaker or voice recognition differs from speech recognition in that the former recognizes and identifies a speaker using voice biometrics and the latter analyzes what is being said. Voice biometrics include both physical characteristics, such as the shape of the vocal tract responsible for articulating and controlling speech production, and behavioral characteristics such as pitch, cadence and tone, etc. Voice waveform recognition, a method of verification that has been used for many years with tape recordings in telephone wiretaps, is now being used for access to proprietary databanks in research facilities.
Hand geometry is being used in industry to provide physical access to buildings. Earlobe geometry has been used to disprove the identity of individuals who claim to be someone they are not (identity theft). “Ears are unique,” says Michael Boczek, the President and CEO of Descartes Biometrics, a company that specializes in mobile ear detection security apps. “It’s stable and enduring, which means it changes very little over the course of one’s life. That’s also true of fingerprints, but less true of facial recognition.”
Vein ID’ technology
The concept of a ‘Vein ID’ technology brings further benefits, finger-vein remains the same throughout the life, and is completely unique to you. An electronic reader maps the user’s finger veins, generating a unique key. The beauty with your vein is that it sits below your skin, and can only be seen when shining infrared light through your finger, making it much more difficult to see and replicate. “While all biometrics have their strength and weaknesses, finger-vein is the most secure,” claims director, Simon Binns of Sthaler, the company behind the Fingopay technology.
Chen Haibo, DeepBlue’s founder and chief executive, says vein-pattern-recognition technology is the safest and most accurate integrated biometric system developed to date. It is also key to many other products designed in the company’s lab. “Our veins are unique and don’t change over time,” Chen says. “So even if you registered when you were a baby, the system will recognise you as an adult, too. External features, like fingerprints or faces, can be copied and altered and raise privacy concerns.
It could soon be possible to search crime-scene DNA for links to nearly all people of European descent who live in the United States. A new study reveals how easy it is to identify a particular person on the basis of their DNA, by connecting them to very distant relatives who have chosen to use consumer-genetics services. The technique works best for Americans with European ancestry, because they make up the vast majority of those services’ customers.
Mobile Bio metrics
Biometrics are being introduced in smartphones and many wearable devices like smart watches, ear-pods, bands, and eyeglasses. These devices have biometric identification capabilities and can identify an individual’s biometric traits like heart rate and blood pressure.
Apple has included TouchID in every iPhone from the 5S onwards, and Microsoft has included a face scanning unlock feature with Windows 10. Google offers glass wearable technology with an optical head-mounted display that enables individuals to access their credentials with voice and facial recognition and has the capability to perform individual identification and verify customers. The law enforcement agencies in Dubai are planning to use Google Glass with a facial recognition software for field operation to capture photos of individuals and search their faces in the criminal database to identify any potential suspects.
The team of the Institute of Laser and Plasma Technologies at the National Researcj Nuclear University MEPhI has developed a system of continuous authentication of mobile device users based on behavioral biometrics. Behavioral biometrics methods monitor a user’s habitual parameters while handling a device, thereby determining who is using a smartphone—the owner or another person. Each person has a unique and inimitable style of handling the phone, and the system is based on identifying these unique characteristics.
Such an authentication system is convenient because, unlike passwords or fingerprints, behavioral biometric features cannot be lost, copied, stolen or faked. This ensures a high degree of protection for the device from outside interference. “The scientific novelty of our project is that for the first time we applied the technology for data analysis, machine learning and artificial neural networks to provide continuous authentication of mobile device users according to their behavioral biometric characteristics. The sensitivity of the sensors used in modern smartphones allows you to select the behavioral characteristics of each user and to use authentication with high accuracy on the basis of aggregate data coming from the touchscreen and other sensors,” said the project leader Konstantin Kogos.
MasterCard has partnered with the biometrics company Nymi to test heartbeat authentication for credit card purchases. Wearable biometric identification devices with different biometric authentication capabilities are available in the market for various purposes. EyeVerify, works by scanning the blood vessel patterns in the whites of your eye by using a selfie taken with a smartphone. Other mobile phone companies have built devices that use infrared cameras to scan irises.
Still, biometric data isn’t 100% secure. Just last year, 5.6 million federal employees’ fingerprint images were stolen. Databases get hacked all the time, from the IRS to Target to hospitals and banks, Universities are hacked every year, medical records, the IRS, banks, dating websites, the list goes on.
Second, physical biometrics can be captured, and in many cases sold, used again or synthesized with fake IDs. Third, there are concerns around bias, which has caused large organizations like IBM to withdraw or scale back from facial recognition technologies. Gartner’s Market Guide for Identity Proofing and Affirmation highlighted that “An increased awareness of bias in machine-learning-based systems reveals that the facial recognition algorithms used in an important class of identity-proofing products have demonstrated demographic bias in their performance.”
Biometric verification does not verify the authenticity of identity data; only that the person verifying is the same who registered the data. Biometric verification on a device helps prevent a fraudster from using a stolen device to falsely claim the identity of the owner, but does not prevent them from establishing accounts with fraudulent information.
Biometric solutions are still not perfect and the technology can make mistakes. Recognition software has yet to mature, so it can misread an image and block access to the authorized user. “Facial recognition is a good example of biometric authentication that can still prove challenging to implement as it tends to be prone to a high false positive rate” Siân John, EMEA chief strategist at Symantec. Other variables also reveal mobile biometrics’ weak spots. Scanner hardware can malfunction if it gets smudged or scratched.
However, the current state of biometrics is still facing challenges to successfully mitigate terrorist activities and other digital based financial theft crimes. To turn-over the situation, the market observes a range of research and development activities to integrate biometrics with artificial intelligence. The advanced software algorithm platform of the artificial intelligence (AI) processes information provided by biometric technology to detect and prevent suspicious activities in a bid to counterfeit cyber and physical threats in the community.
Eventually, we could even see biometrics able to identify people by their brain waves. Since as early as 2013, researchers have been studying a way to record brain signals using an electroencephalogram, a monitoring test historically used to diagnose epilepsy, tumors and other disorders.
Brian fingerprinting is a computer based test which discovers, documents & offers evidence regarding crimes. It is also used for identifying people involved in terrorist cells. The brain fingerprinting technology measures electric brain waves by measuring the recognition of familiar stimuli in response to data presented on a screen. The increase in technological advancements across the globe fuelling the demand for biometrics, due to which the demand for brain fingerprinting is replacing traditional ways of detecting crimes & counter terrorism.
The increase in use of this technology in government sector to solve crime activities, and prevent public benefit activities or fraud voting’s expected to boost the global brain fingerprinting technology market growth over the forecast period. The increase in investments in research and development activities is expected to drive the global brain fingerprinting technology market growth. Also, the growing adoption of AI and machine learning in healthcare industry will positively influence the market growth.
Biometric Authentication Through Behavior ID
Behavioral biometrics analyzes an online user’s physical and cognitive digital behavior. Behavioral biometrics identify and measure human activities, such as keystroke dynamics, voice print, device usage, signature analysis, error patterns (accidentally hitting an “l” instead of a “k” on two out of every fifth transaction), etc. Such behavioral biometrics are typically used as an additional layer of security, along with other credential or biometric information. Most physical biometric solutions systems authenticate the user only once and usually at the beginning of an action, such as logging into a device or opening a door. Behavioral biometric technology attempts to fill the gap of authentication in a scenario during an action.
It has emerged as a breakthrough cybersecurity technology that identifies people by how they do what they do, and identifies behavior patterns of legitimate users vs human or non-human cybercriminal actors. It is an advanced solution for fighting cybercrime and detecting fraud in an age when criminals have more access to our personally identifiable information (PII) and more sophisticated hacking methods than ever before.
Mobile identification company TeleSign launched Behavior ID , an online application that tracks a user’s behavior to prevent cybertheft. The application records behavior such as how a user moves their mouse, presses a touch screen, or the way they type. This increases the level of identity assurance for every user account a company has, according to Steve Jillings, CEO of TeleSign. “The power of Behavior ID is its ability to adapt to the user, transparently producing a digital fingerprint from a user’s behavior to confirm their identity and develop an ongoing authentication without requiring the consumer to do anything,” he said in a press release. “Best of all, these unique biometric patterns are extremely accurate, from the way we move our hand on a mobile device screen or with a mouse, it is virtually impossible to precisely imitate another person’s behavior.”
Security of Biometrics
Biometric data isn’t immune to these attacks. For instance, at the CCC conference in 2014, a security researcher called Starbug used a simple 3D printed mold to construct a working model of the German Defence Miniser’s fingerprint which was based on a high-res photograph of the minister’s hand. And researchers at Michigan State University released a paper that describes a method for spoofing a fingerprint reader using conductive ink printed with an ink jet printer in less than fifteen minutes.
Voice recognition application ‘Siri’ has also faced several security issues. In 2011, a China-based hacker group managed to jailbreak the iPhone 4 and run a full version of Siri which allowed them to steal sensitive information from the users who installed the app. At the same time, according to various security researchers, a sample of user’s voice can be collected in various ways including making a spam call, recording person’s voice from a physical proximity of the speaker, mining for audiovisual clips online and compromising cloud servers that store audio information.
In fact researchers from mobile security firm Vkansee were able to break into Apple’s Touch ID system with a small piece of Play Doh just last month at the Mobile World Congress—similar to what security researcher Tsutomu Matsumoto a did with a gummy bear over a decade earlier with another fingerprint sensor. Many commercially available iris-recognition systems are easily fooled by presenting a high-quality photograph of a face instead of a real face.
Biometrics, on the other hand, are inherently public, he argues. “I do know what your ear looks like, if I meet you, and I can take a high resolution photo of it from afar,” says Bedoya. “I know what your fingerprint looks like if we have a drink and you leave your fingerprints on the pint glass.” And that makes them easy to hack. Or track.
Biometric data is typically stored in databases that are subject to the same security concerns as any other network system. That means strong authentication measures must be in place to prevent unauthorized users from gaining access. Encryption for data-at-rest/data-in-transit and multi-factor authentication protocols should all be considered fundamental security elements of any network storing biometric data. Another good way of keeping your biometric data safe is by keeping your software and firmware up-to-date. Any time your device manufacturer or software manufacturer informs you about the latest security patch or software update, make sure you install it immediately.
AI in Biometrics and Security
The current state of biometrics is still facing challenges to successfully mitigate terrorist activities and other digital based financial theft crimes. To turn-over the situation, the market observes a range of research and development activities to integrate biometrics with artificial intelligence. The advanced software algorithm platform of the artificial intelligence (AI) processes information provided by biometric technology to detect and prevent suspicious activities in a bid to counterfeit cyber and physical threats in the community.
Many research institutes and corporates are keen on utilizing this opportunity to mark a difference by integrating biometric technology such as the facial recognition with AI to use in video surveillance. For instance, Hitachi has developed a technology for surveillance which has claimed to process a variety of information with improved processing time. The technology works supposedly by simultaneous calculation of multiple parameters, which is not the standard practice.
As cybersecurity issue is a growing concern, the surge in smartphone adoption, urbanization, and digitization has increasingly driven the biometrics industry. This has been of specific concern in the financial sector, which is leading the adoption of biometrics in the commercial segment. The adoption of voice biometrics in the banking and telecommunication industries has proved to be profitable in terms of operational efficiency and customer satisfaction. Interestingly, in recent times voice biometrics has been attempted to combine with AI in a bid to directly communicate with chatbots up to the emotional level of understanding. This could probably reduce the need for human operators in call centers in the future while protecting employee perpetrated data leaks.
In April 2017, IARPA awarded AI biometric solutions provider Crossmatch a contract of $5.8 million to “develop next-gen biometric presentation attack detection technologies.” In June 2017, it funded a four-year $12.5 million contract to SRI International, an independent, nonprofit research center to “address vulnerabilities in the current biometric security systems,” specifically, fingerprint, iris and face scanners.
The Sept. 11, 2001, terrorist attacks accelerated the growth of U.S. military applications using biometric technologies to identify the enemy and to improve security and surveillance.
The U.S. Army’s wearable authentication tokens, which supports identity authentication, have been in continuous iterative development since the original prototype system was completed in 2019. The wearable identity tokens combine public-key–based credentials with advances in the commercial wireless payment industry and flexible hybrid electronics, according to Ogedi Okwudishu, a computer scientist within the U.S. Army Combat Capabilities Development Command in the C5ISR Center’s Tactical Network Protection Branch.
Soldiers will use them to connect wirelessly and securely via Bluetooth Low Energy to an end-user device. They must use a PIN as a second authentication factor to get the network system to grant them access to a tactical system. Once out of range of the system, they are logged out.
The U.S. Army Research Laboratory (ARL) has conducted experimental tests that combine facial-recognition technology with thermal imagery to allow soldiers to better identify persons of interest in the dark. The new technology uses AI, machine learning, and infrared cameras to identify facial patterns by detecting radiated heat from the skin.
Thermal and polarimetric-thermal
A team of researchers from Maryland’s U.S. Army Research Laboratory have developed a new technique exploiting thermal-imaging that potentially could help improve facial-recognition performance that is otherwise hindered by makeup. Developed by Doctors Nathaniel Short, Alex Yuffa, Gorden Videen, and Shuowen Hu, the new method compares visible, conventional thermal and polarimetric-thermal images of faces before and after the application of face paint.
The researchers have been using polarimetric-thermal imaging, a maturing thermal mode that records the polarization-state information of thermal infrared emission, to collect geometric facial data from thermal imagery. This method could provide several advantages over conventional thermal imaging when matching faces with paints or cosmetics, said Short.The research team describe their findings in The Optical Society (OSA) journal, Applied Optics.
Traditional facial-recognition systems are based on matching clear and well-lit photos captured in the broad light. Recognizing faces using visible-light imaging depends on capturing the reflected light from the edges of facial features. This can be difficult when faces are covered with cosmetics as they tend to distort the perceived shape of the face and degrade the face-recognition accuracy of visual imaging due to the different spectral properties of color pigmentation. In comparison, infrared, thermal signature is naturally emitted from the human face and can be attained passively in low-illumination conditions and even if face paints or cosmetics cover the skin’s surface.
Despite this promising research, Short emphasizes that the development of the new facial-recognition technique is still in its initial stages and that many challenges still exist. “One of the major challenges is the limitation of the existing polarimetric-thermal facial database,” said Short. “Large sample pools are needed to develop and train complex machine-learning techniques such as neural networks computer programs that attempt to imitate the human brain to make connections and draw conclusions.” Another key challenge is in developing algorithms that bridge the large modality gap between visible imaging and polarimetric-thermal imaging for cross-spectrum recognition.
Advancements in AI and machine learning have resulted in better integration of thermal facial detection to facial recognition, said Dr. Sean Hu, team lead/electronics engineer in the U.S. Army Research Laboratory Intelligent Perception Branch. Hardware development is in progress, and the next iteration of a handheld binocular prototype with on-board thermal facial-recognition capabilities is under development, he said.
In 2019, an initial handheld binocular prototype was developed for short-distance scanning. A more advanced prototype that is capable of imaging at a long distance is being developed, Hu said. ARL is also working to improve its algorithms. The Army’s corporate research laboratory and a team of scientists have already developed the initial algorithm that ties the data into the integrated software and hardware platforms. The ability of thermal imaging is made possible by detecting radiated heat from the skin or objects, and when properly calibrated, imaging can measure the absolute temperature from the skin, Hu noted. The biggest advantage is that thermal imaging can capture distinctive features behind a face mask covering. It allows night-time facial recognition in low-lighting areas. However, the challenge is that the imaging works only with thin, very tight-fitting facial coverings.
The U. S. Army has modernized a 20-year-old biometric database with a new software update to help soldiers patrolling at foreign checkpoints to identify persons of interest in real time. “The original database relied on software to create relationships between tables,” said William Daddario, an engineer in the CCDC C5ISR Center’s Cryptographic Modernization Branch. “We built the relationships into the database. This is a more efficient way to operate.”
So if one change is made in one area, it propagates throughout the whole database. The handheld Biometrics Automated Toolset-Army (BAT-A) is used by soldiers to collect and process biometric identification data such as irises, fingerprints, and facial images. The subject can wear glasses or contact lenses while an iris image is taken. The collected data is compared with the data stored in the Department of Defense’s database of 1 million biometric entries.
The global identity and access management market will be worth $24.1 billion by 2025 according to another new report from MarketsandMarkets, with growth driven by increasing funding from venture capital firms and growing investments in IAM technology. The market was estimated at $12.3 billion in 2020, and is expected to grow at a 14.5 percent CAGR.
The global biometrics market is forecast to reach $82.8 billion by 2027, growing at a 19.3 percent CAGR from an estimated $24.1 billion in 2020, according to a report from Global Industry Analysts. The future growth of the biometric system market is expected to be driven by rising use of biometric technology in financial institutes and healthcare sectors, government initiative in adoption of biometrics system, and increasing use of biometric systems in criminal identification.
The mobile biometrics market is expected to be neutrally-impacted by COVID-19, with an increasing number of mobile payment transactions as banks rapidly digitize, a new Technavio report suggests. Market growth will decelerate by 19 percent over the forecast period, but the mobile biometrics market will still grow by $15.63 billion overall, a 21.53 percent CAGR, according to Technavio. Of that growth, 43 percent will come from the APAC region.
The global market for voice biometrics will grow by $2.6 billion, or 19 percent CAGR from 2020 to 2024, driven in large part by law enforcement, BFSI and health care applications, Technavio writes in another report.
The new ‘Automatic Identification and Data Capture Market…’ report from MarketsandMarkets forecasts a leap from $40.1 billion in 2020 to $80.3 billion in 2025, a 14.9 percent CAGR for an industry that includes barcoding solutions, wearables, and radio frequency identification (RFID) solutions. Key factors include the continuing growth of ecommerce, the use of smartphones for QR code scanning and image recognition, and rising adoption of AIDC solutions for minimizing queuing and transaction time. Increased adoption by financial institutions to provide data privacy-preserving security is also expected to impact the market.
The U.S. market is estimated to be worth $7.1 billion this year, half of the market size China is expected to represent in 2027 after growing at an 18.5 percent CAGR. The Asia-Pacific region not including China is expected to be worth $10.1 billion by 2027. Iris recognition will grow at an 18.8 percent CAGR, according to GIA, and reach $10.3 billion by the end of the forecast period.
The global military biometrics market is highly competitive, as its vendor landscape is marked by the presence of several large global players. The dominance in the market is however held by five major players – 3M Cogent, NEC Corporation, M2SYS Technology, Crossmatch, and Safran. Together these companies accounted for nearly 61% of the global military biometric market in 2016. Other Major players in this market include Fujitsu Ltd. (Japan), BIO-Key International, Inc. (U.S.), Precise Biometrics AB (Sweden), Secunet Security Networks AG (Germany), Thales SA (France), Aware, Inc. (U.S.), Cognitec Systems GmbH (Germany), Fulcrum Biometrics, LLC (U.S.), Daon, Inc. (U.S.), and Facebanx (U.K.).
Despite recent innovations, integration complexities still prevalent in military biometric technologies are hindering the market’s trajectory. It is very important to correctly configure any biometric system and feed it with proper data for it to function correctly. Any loophole in the integration process can cost organizations big time. Also if the system gets compromised with at any point, it could result in security breach, for it is highly time- and labor-consuming to reset the whole system. Such integration complexities pose big risk to the defense sector as it involves highly confidential data related to their country. This could be a major deterrent for the military biometrics market.