Why Facial Biometrics Are Critical to High Assurance Identity Verification

Authentication comes down to one simple question: is this person really who they say they are?

As password use continues to decline, passwordless methods, such as SMS OTPs and passkeys to call center verification and facial biometrics, are replacing them. But not all passwordless approaches deliver the same level of security, privacy, or user experience (UX).

According to Ping Identity’s Global Consumer Survey, biometric authentication ranked as the top feature globally that would increase trust in organizations, underscoring the importance of utilizing this capability to achieve verified trust in an era dominated by AI-driven threats.

Let’s take a look at high-assurance environments where identity assurance truly matters—consumer sectors like banking, fintech, insurance, and gaming, as well as workforce settings such as heavy industry, retail, food production, and organizations with numerous third-party identities spread across multiple partner/B2B relationships. In these environments, proving identity across every digital interaction is critical.

Facial biometrics are quickly becoming a primary way to authenticate users in digital environments. Unlike fingerprints, which require special hardware and physical contact, facial recognition works on mobile devices, desktops, and kiosks using standard cameras, making it more accessible and scalable.

It’s also important to distinguish biometric authentication from the biometrics used in identity verification (IDV). IDV biometrics are typically used once, during onboarding, to match a person to an identity document, while facial biometric authentication is used again and again to confirm that the same enrolled person is returning.

What is Facial Biometric Authentication?

Facial biometric authentication is a method of verifying identity by comparing a live facial scan to a previously enrolled biometric reference. Rather than relying on knowledge (like passwords) or possession (like devices), it evaluates inherent physical characteristics to confirm identity in real time. This process typically includes advanced matching algorithms and presentation attack detection (PAD) to ensure the interaction is genuine. Because it operates through standard cameras and can be embedded across digital channels, facial biometrics provide a consistent and scalable way to verify users without introducing additional friction into the experience.

High Assurance Identity Verification

High assurance identity verification refers to the ability to establish and continuously maintain a strong, defensible link between a real person and their digital identity. It’s not just about initial proofing during onboarding, but ensuring that every subsequent interaction can be trusted with a high degree of confidence. This level of assurance is essential in environments where the cost of impersonation is high, requiring technologies that can withstand sophisticated threats like deepfakes, synthetic identities, and account sharing. By anchoring authentication to a verified biometric and maintaining that linkage over time, organizations can enforce stronger security controls without degrading the UX.

Before diving deeper, it’s helpful to place facial biometric authentication in the broader context of passwordless methods, because it solves a different problem: proving who the user is, not just that they control a particular device or account.

There are several types of passwordless technologies—some are better suited for consumer use, while others are designed primarily for workforce environments.

The key difference between these approaches is the level of identity assurance they provide, meaning the degree of confidence that the person authenticating is truly the verified individual and not just someone in possession of a device or account. In high-assurance environments, that distinction matters.

Many passwordless methods improve convenience, but few prove the person logging in is the one who enrolled. SMS OTPs, email links, and even passkeys only confirm access to a device or account—not the real individual using it.

Facial biometric authentication is different. By linking a real, living person to their digital/online identity using traits like a face, it makes impersonation much harder and becomes critical for high-risk environments—providing the strongest confidence that the person being granted access is who they say they are.

There are three primary models for facial biometric authentication—local, centralized, and decentralized systems—each with distinct strengths and trade-offs.

What Are Local (Device-Native) Biometrics?

Facial biometric data is processed and stored on the user’s device. This includes solutions like FaceID.

Pros:

• Strong privacy: The biometric data never leaves the device.

Cons:

• Limited across devices: Biometrics are tied to the operating system. A person may not be able to use Face ID to authenticate on another company’s device.
• Lower identity assurance: Face ID does not compare your face to the one used during account opening or onboarding. It only checks against the face currently enrolled on the device. If someone knows the device PIN, they can re-enroll their own face and access apps that rely only on Face ID—in short, entire families can enroll their faces on a FaceID account. The app itself cannot tell that this is not the originally verified person.
• No objective performance standards: Unlike professional-grade biometric engines, device-native tools lack public, third-party data on False Acceptance Rates (FAR) or False Rejection Rates (FRR). This forces organizations to blindly trust a wide range of uncertified hardware without knowing the actual level of security being delivered.

What are Centralized Biometrics?

Facial biometric templates are stored on a central cloud server.

Pros:

• Works across devices and platforms: A user can enroll on one company’s device and authenticate later on another. This is especially important in workforce environments.
• Stronger identity link: The face used at login can be matched to the face captured during onboarding, IDV, or “Know Your Customer.”

Cons:

• Greater privacy risk: Storing biometrics centrally means if the server is compromised, the biometric data could be exposed.
• Poor UX: Typical central biometric solutions are slow, often taking 5 to 7 seconds to authenticate due to how they process and store data. Some also use flashing lights for liveness detection, which can be intrusive or even cause adverse health effects for sensitive users.

What are Decentralized Biometrics?

Decentralized biometrics is a modern approach that combines the cross-device flexibility of centralized systems with the privacy of local biometrics.

Instead of storing a full facial biometric template on a device or in one central server, decentralized systems use cryptography and privacy-preserving techniques, such as sharding or secure multi-party computation (sMPC), to split and protect facial biometric data. No complete facial biometric template exists in any single place.

Pros:

• Strong privacy and security when properly implemented.
• Works across devices and platforms.

Cons:

• Not all “decentralized” solutions truly protect privacy. Architecture matters.
• Still relatively new, so some organizations may lack familiarity or trust in the model.

Types of Decentralized Biometrics

There are two main approaches to decentralized biometrics: sharding and secure multi-party computation (sMPC).

Sharding splits biometric data into pieces and stores them across multiple servers. During authentication, each server processes only part of the data. While this reduces risk, those servers are often controlled by the same vendor. Even if they are not, if enough pieces are accessed, the data can be reconstructed, defeating the point of the system entirely.

sMPC, used in Ping Identity’s zero-knowledge biometrics capability, takes a stronger approach. The biometric is transformed on the device into a cryptographic representation that is mathematically impossible to reverse or reconstruct once on the cloud, even if intercepted or breached. No biometric data is stored on the device or in the cloud in a retrievable or reconstructable form.

The idea behind sMPC is often explained through the “Millionaire’s Problem”: two millionaires want to know who is richer without revealing their actual wealth. sMPC allows them to find the answer without exposing their private inputs. In the same way, biometric matching can happen without ever revealing or reconstructing the underlying biometric data.

sMPC delivers the best of both worlds—local and centralized:

• Usability: Like centralized systems, users can enroll once and authenticate across devices and operating systems.
• Identity assurance: Like centralized systems, authentication links back to the biometric captured during onboarding or IDV, preventing FaceID-style re-enrollment attacks.
• Privacy: Like local systems, there is no biometric data stored on the cloud in retrievable or reconstructable form. Even if the server is, there is nothing meaningful to steal.

Facial biometric authentication may feel like a major shift, but compared to legacy methods, it often delivers fast, measurable ROI. Passwords, PINs, key fobs, OTPs, and call centers are not only weaker—they’re also expensive to run and support.

In consumer scenarios, every password reset or step-up adds cost. Contact center calls are expensive, and SMS OTPs incur per-message fees.

A leading European digital bank replaced passwords and SMS OTPs with privacy-preserving facial biometrics for step-up and recovery flows. Customers now authenticate with a single glance instead of calling support or waiting for codes, cutting telecom spend and helping drive a six-figure annual saving while reducing account takeover fraud by nearly 80%.

In workforce environments, time saved is money saved.

A large US food production company introduced facial biometrics on wall-mounted shared tablets for employees entering the building each morning. Authentication time dropped from roughly 30 seconds to about seven seconds, with the facial biometric step adding only around a second. At scale, those seconds reclaimed per login improved line productivity and reduced password-related support tickets.

Taken together, these outcomes show that facial biometric authentication consolidates fragmented journeys, drives down operational spend, and pays for itself quickly in high-volume, high-assurance environments.

Facial biometric authentication has already reshaped digital identity, but its most significant progression is still ahead.

It’s moving beyond mobile apps and into web browsers, desktops, shared devices like kiosks, and remote access platforms such as VPNs and VDIs. The future is channel-agnostic, meaning facial biometrics will work across devices and environments, wherever users need access.

Facial biometrics will continue to extend beyond login. They’re set to power digital identity wallets, biometric signing and encryption, frictionless payments, account recovery and device binding.

At the same time, innovations in privacy-preserving biometrics—like zero-knowledge architectures—are ensuring that this expanded use doesn’t come at the cost of user trust, keeping biometric data protected while still enabling seamless authentication experiences.

The direction is clear: Fewer passwords, fewer tokens, and no security questions, just a secure, private, and portable identity built around who you are.