Using a fingerprint to pay your bills online or scanning your face to unlock your smartphone is part of our daily routine. These are also common use cases of biometric identity verification.
More companies going digital consider customer biometrics to be the most secure and easy way to onboard and authenticate new users. This approach is reliable and convenient, but it also has some limitations.
In this article, we’ll give you a clear idea of what biometric verification is, which options you have when building this kind of authentication system, and what to take into account when implementing biometrics into your identity verification flow.
What is biometric verification?
Biometric verification is a way to identify and validate users by using their biometrics as a unique and recognizable proof of identity.
This is one of the most reliable methods of identification and authentication. Although these terms are still used as synonyms in many articles on the web, there is actually a big difference between them.
Biometric identification is the process of identifying an individual by obtaining their biometrics, e.g. a photo or fingerprint. This is one of the stages of customer onboarding when a user signs up for your service.
Biometric authentication is the process of identifying a person as a current user based on their biometrics obtained during registration. At this stage, the customer uses their biometrics as one of the authentication factors in addition to a login, one-time password, etc.
Simply put, during the identification process, you can identify: Who is X? The authentication process enables you to check: Is this really X?
Biometrics gives you an answer to both questions.
The types of biometrics used for identity verification
Although there are many characteristics that can be used to describe a person, only certain biometrics can be used as an individual user’s identifier. Typically, these stay invariable during one's life. For example, hair color and style are bad choices for determining someone’s identity.
Here are some examples.
Technically, any of these traits can be the core component of a biometric verification system. However, there are more and less widely used options. Let’s take a closer look.
Since facial features such as the shape of the chin or the distance between the eyes are generally unchanging throughout adulthood, using the face as a biometric identification and authentication factor seems to be a reliable approach. However, user scenarios involving children and elderly people are more tricky since their facial features can change significantly over a short period of time. That’s also the reason why minors can obtain only short-term IDs that are valid for up to five years after issuance. Seniors and people who are older than 40 typically obtain identity documents for up to ten years.
This type of biometric verification involves a face matching algorithm, in which two photos belonging to the same person—the one being presented and the one on record—are compared. For instance, a user’s selfie is screened against their photo stored in the company’s database.
On the software end, there is no “photo” as we understand the meaning, but a digital descriptor—a user’s token containing all the details the system needs to identify and match a selfie with a particular individual. The descriptor doesn’t include any personal data, which makes facial recognition a secure technology in terms of customer privacy.
Facial recognition has a high adaptation rate across diverse sectors. According to a recent Regula survey, 56% of companies in Banking, Telecoms, Aviation, and IT use this process for remote customer verification. What’s more, 27% of respondents claim it is the most effective online authorization method.
The growth of the facial recognition market also proves the high interest in the technology. As Deloitte analysts admit, the segment is showing a significant increase: from $3.8 billion in 2020 to $8.5 billion by 2025. Facial recognition is deployed in the financial, retail, healthcare, education, and hospitality sectors, among many others. However, the use of this technology is under strict supervision by governmental agencies and human rights defenders. Regulations such as the EU's AI Act serve as a deterrent for broader implementation of the technology.
This biometric verification method considers unique characteristics of fingerprints like ridges, whorls, and other patterns as proof of identity. Usually, it is accompanied by another user-related asset, for instance, an ID that is linked with them. That’s because issuing passports and other secure IDs involves collecting and storing the document holder’s fingerprints in the government's database.
Anytime a customer presents their fingerprint to access the service, the system matches the presented fingerprint with the one stored on the service’s side or in their ID to authenticate the user.
This is another widespread technology: 60% of businesses surveyed by Regula use it for verification. The increase in revenue of fingerprint identification system providers—from $5.32 billion in 2022 to $15.42 billion by 2028—also proves the popularity of this type of biometric verification. Commonly used application scenarios of the technology include mobile authentication, civil identity systems such as border security and voting, and physical access control.
Voiceprints also feature among the most frequently implemented types of biometric verification. In this procedure, the user’s stored identifier is a voice sample that is matched against their voice when issuing commands during an online session, for instance, when conducting mobile banking.
To validate the voice input, the software behind the scenes splits the voiceprint into multiple frequencies and compares it with the sample from the database. Depending on the recognition scenario the system follows, the user should say a particular phrase or password (text-dependent speaker verification) or speak freely (text-independent speaker verification) at the authentication stage.
Voice recognition is an easy-to-implement technology; however, it provides lower protection against fraud compared to the systems described above. For instance, 37% of the organizations interviewed by Regula have experienced deepfake voice fraud. Plus, 45% of companies declared that this type of fraud method is a real threat.
Nevertheless, voice recognition usage is growing, spurring the fivefold increase in the global market in the coming decade, projected to reach nearly $50 billion by 2029. Plus, the technology is used in other applications, including mobile devices (speech-to-text, voice dialing and search), audio transcription applications, and many more. Also, voice authentication is applied to multi-factor verification systems, for instance, in mobile banking.
While all the previous technologies mentioned can be deployed both online and offline, iris verification is an on-site procedure. However, considering the intrusion level, iris recognition tops the biometrics list, followed by fingerprint and face recognition methods. This approach implies user identification by analyzing one or two of the user’s irises, which have quite complex, stable, and unique patterns.
In contrast to face recognition, where a static photo can be used as a live sample, you need a pricey scanner with a high-resolution video camera to perform iris verification. Then, the image is converted into a digital template containing numerical data with the iris parameters, which are compared against the samples from the database.
Despite some limitations, the technology remains in high demand. Statista values the iris recognition market at about $4 billion in 2022, with an expected growth of $8 billion by 2027. The fields of application range from national ID programs to physical access control and border defense.
Other types of biometric verification
There are even more types of biometrics used in identity verification, but the next ones on our list are used much less frequently. Among the reasons are the higher cost and purpose-specific nature of such systems.
Among the less common biometric verification types are identification by ear shape, hand geometry, and vein pattern. All these approaches involve matching a live sample and a photo taken during registration to verify a user. Since acquiring a sufficient sample image is a must for these scenarios, such approaches can be applied only offline. They may be used for ATM security, door security, and login management systems.
Signature, keystroke dynamics, and gait recognition are examples of biometric verification based on behavioral features. For example, signature verification helps verify an individual by their handwriting; this approach is applicable for use by financial institutions, election monitors, and other entities. The biometric system analyzes the sample by considering its shape, spatial coordinates, pressure, inclination, etc. Then, these parameters are cross-referenced with the sample signature provided by the user.
Tracking keystroke dynamics, such as dwell and flight time during typing, helps manage access-restricted products and verify users following security procedures. This is commonly used as an extra layer of security in multi-factor authentication systems.
Gait recognition, which identifies an individual's walking patterns, may help monitor people in crowded environments and public places, and verify people before permitting them access to restricted areas. Gait-based authentication systems apply to many industries, including Healthcare and Banking, but they require a collection of sensors, cameras, and computers to perform.
Biometric verification standards
Biometric authentication and identification systems are designed according to strict standards that govern the collection, storage, and sharing of data, as well as the performance metrics and accuracy assessment of the system. IDV vendors must adhere to these standards when developing their solutions. The key regulators in this field include the International Committee for Information Technology Standards (INCITS), the National Institute of Standards and Technology (NIST), and the Joint Technical Committee (JTC).
Here are the major standards for the most commonly used types of biometrics:
Biometric verification system: An explainer
Depending on the type of verification you use, there are some specific technical features. Simply put, any biometric verification system consists of the following elements:
Identity verification software
You can also get a user’s biometrics with other identifiers such as name, address, etc. during the identification process—commonly known as enrollment or onboarding—which is the first interaction between the user and the system. Depending on the type of biometrics, specific equipment (an iris scanner) or the user’s smartphone—camera or fingerprint scanner—can be used to obtain biometrics. Mobile-friendly options are available in remote authentication scenarios.
It’s also worth noting that there are multi-factor biometric verification systems where more than one type of biometric information should be submitted to complete the procedure. Usually, these systems are deployed on-site.
Here’s a brief rundown of a typical biometrics collection procedure. A biometric sample is collected from the user (photo, fingerprint, voiceprint, etc.). The sample lands in the sample database. Typically, it’s converted into a file containing essential information only. That is, there are not dozens of users’ photos there, but dozens of descriptors of users’ photos, which are about a few KB each. This means that extra storage capacity isn’t a must when building a biometric verification system.
Identity verification software is the core of the process. It collects and processes the user’s biometric data at the first stage, and after that, checks for a match between the sample and any biometric data submitted in the future.
Biometric verification use cases
Using behavioral or morphological traits of individuals—either customers or employees—as authentication factors is a common procedure in many industries.
In onsite scenarios, biometrics like fingerprints, iris, or vein patterns are components of internal security systems that manage access to secure directories and restricted areas, such as data centers, server rooms, and laboratories.
Face biometric access control systems are widely deployed in the Aviation and Transportation industries. Self-check-in kiosks at airports, as well as airline mobile apps with face recognition modules—for example, the ones provided by WizzAir and airasia—allow passengers to enroll, enter the gate, and board quickly. Also, the use of biometrics helps prevent unauthorized individuals, including drug smugglers, from entering terminals at countries’ ports. Belgium is poised to deploy such systems at its marine hubs. They are also commonly used in major US airports.
There are also many use cases in online biometric verification. Some countries implement biometrics into the personal data databases processed by government agencies. For instance, the Aadhaar ecosystem employed by the issuing body of Indian ID cards may include the holders’ iris patterns and fingerprints. This enables ID bearers to access a plethora of services after instant biometric authentication.
Larger banks take advantage of the technology as well. For instance, customer biometric verification is the first onboarding step for new UBS clients. Biometric ID scanning and face recognition make the process fast and frictionless for customers, while still keeping it secure.
Cryptocurrency and Gambling companies harness biometric authentication as well, primarily to validate users attempting to withdraw money.
The risks and rewards of using biometric verification
The flip side of the process is that fraudsters can also exploit the technology when attempting attacks. They can use silicone masks and mannequins, create voice and video deepfakes, and even reconstruct fingerprints to deceive verification systems based on different types of biometrics.
That’s why any reliable identity verification system based on biometrics must include biometric fraud prevention components.
Another concern refers to data privacy. Many (but not all) countries have specific regulations dedicated to the use of biometrics in customer identification by organizations and companies. For instance, using face recognition in public is restricted by the authorities of some big American cities, including Boston, Massachusetts and Portland, Maine.
Additionally, there is some inconsistency in the law on biometric data storage. As an example, in the USA, only Illinois and California have laws on facial recognition data storage, while other states don’t yet.
Moreover, there may be biometric verification software hiccups due to imperfections in the algorithm under the hood. False negatives—in which the system wrongly indicates that a condition hasn’t been met—are one of the most common obstacles faced by companies that use biometric verification. As a result, genuine customers may land on the blacklist, which may cause financial and reputational damage. Since many biometric verification providers are SaaS companies that aggregate data from hundreds of clients, sometimes it’s difficult to get a particular customer out from under the ban.
A positive example: Schengen visa verification flow
Biometric verification is used in the Schengen Area. Any individual applying for a Schengen visa must undergo fingerprint scanning. All ten fingerprints, along with the data provided in the visa application form, are stored in the Visa Information System (VIS). According to the standard, a person’s fingerprint is checked at the border to match the newly obtained sample against one in the VIS. However, a mismatch doesn't prohibit the person from crossing the border. Inspectors just need to conduct further checks to confirm the traveler’s identity.
Despite the potential drawbacks and vulnerabilities, biometric verification, as a part of a multi-factor authentication system, is a good way to minimize fraud and data breach threats, enhancing security for both companies and their customers. The technology is also aligned with the concept of a passwordless future, which has been gaining momentum in recent years.
Usually, biometric data isn’t identifiable without additional context. That is, there are no high-resolution photos of each customer’s fingerprint in a company’s database accompanied with their full personal information like name, date of birth, and home address. One biometric entry is just one unit of information connected to other data about the user via a system of unique indicators. That means it's more reliable than a password-based system, and more resistant in the case of data breaches.
Plus, biometric verification typically provides wide accessibility compared to passwords and one-time codes sent by SMS. This is mainly true for an authentication flow based on facial recognition. While fingerprint or iris scanning may be limited due to an individual's injuries or disabilities, face scanning remains the easiest way to validate somebody’s identity. What’s more, it can be checked in seconds via a user’s mobile device; no special equipment is necessary. That also makes facial recognition one of the most affordable biometric verification components.
How can you benefit from Regula’s biometric verification technology?
As a 100% on-premises identity verification vendor, Regula provides you with robust solutions for biometric verification. Regula's technology offers a secure and reliable way to verify identity in various industries, helping businesses prevent fraud, protect sensitive information, and improve customer experience.
Available both on-site and online, Regula solutions ensure seamless user enrollment and authentication with the use of biometrics via mobile devices, check-in kiosks, identity verification devices, and more.
Book a demo call to learn more about your options.