How Multimodal Authentication improves physical access systems

Oloid Desk
May 20, 2022

Multimodal authentication is a process for capturing biometric traits apart from just face and voice. Also known as multimodal biometrics, this technology combines different types of biometrics, sometimes simultaneously, in a multifactor solution that offers a multitude of authentication options.

With multimodal authentication, biometric traits (e.g., fingerprint, voice, and facial features) are used in conjunction with behavioral characteristics (e.g., keystrokes and gait) to verify a person’s identity. In contrast, multifactor authentication focuses only on user authentication to determine whether the user is genuine—a far less secure system.

Table of Contents

1. How multimodal authentication works

2. Uses of multimodal authentication

3. Different modes of authentication

4. Benefits of multimodal authentication

5. Conclusion

How does Multimodal Authentication work?

Multimodal authentication enables foolproof security that is far superior to that of systems relying on only a single factor. For example, facial recognition software can be fooled with a near-identical photo that matches an individual’s face parameters. That’s why using facial recognition as the sole authentication method isn’t a good option; instead, combining it with other biometrics provides better security.

The preferred multimodal physical access control solution offers both iris and fingerprint scanning, but if hands might be covered by gloves or otherwise occupied, an iris scan can be used for authentication. After traits are scanned individually, a fusion algorithm merges the results and the scores are added to determine whether they meet a preset minimum, facilitating a single sign-on for a yes/no decision.

Uses of Multimodal Authentication

Applications of multimodal biometrics are as varied as the technology itself. Law enforcement has used biometrics (fingerprints, facial recognition, voice, and iris scans) as identification systems for several years. More recently, enhancements in mobile technology have upgraded smartphones into handheld multimodal biometric authentication devices. These systems are increasingly being used as authentication methods in private/individual hands through the advent of smartphones, smartwatches, and other intelligent devices that use single sign-on and mobile access QR authentication.

In enterprise security markets, where flexibility is essential for users with unique needs, multimodal biometrics can be integrated into cloud platforms for mobile apps, particularly in mobile banking.

For a single sign-on option, a strong authentication choice is sent to the end-users. This solution is ideal for border control, law enforcement, national IDs, high-security physical access control, and more.

Modes of Multimodal Authentication

Multimodal authentication can be used in estimation-based, rule-based, and classification-based fusion modes. Estimation-based modes resolve the estimating parameters, whereas rule-based and classification-based methods help retrieve decisions based on specific observations. However, if an observation is collected from various modalities, its fusion score is required before estimation.

1. Estimation-based Fusion Modes

The estimation-based mode includes the extended Kalman filter, particle filter, and Kalman filter fusion methods. These methods primarily analyze multimodal data to deduce the state of moving objects. For example, for object tracking (i.e., determining the position of an object), video, audio, and other modalities are fused to a final result.

2. Rule-based Fusion Modes

The rule-based mode fuses multimodal information with a collection of basic rules-based approaches like MAX, MIN, linear weighted fusion, majority voting, and AND/OR. Customer-defined rules are made for specific application perspectives. The rule-based mode performs well if the quality of the temporal alignment between different modalities is good.

3. Classification-based Fusion Modes

With classification-based modes, multimodal observations are classified into a single pre-defined class via methods such as the Bayesian inference, maximum entropy model, support vector machine, dynamic Bayesian networks, Dempster–Shafer theory, and neural networks. These can be further classified as discriminative (e.g., neural networks and support vector machines) and generative (e.g., dynamic Bayesian and Bayesian inference networks) models in terms of device learning; such discriminative models are neural networks and support vector machines, whereas generative models are dynamic Bayesian and Bayesian inference networks.

Benefits of Multimodal Authentication

Across applications, multimodal authentication delivers significant benefits in terms of user-friendliness, security, and operational considerations.

  • Accuracy: Multimodal authentication is more accurate than reliance on any single biometric trait.
  • Fraud Resistance: The use of multiple biometric traits, especially if authenticated simultaneously, is less susceptible to fraud than authentication based on a single feature like facial recognition.
  • Flexibility: Multimodal authentication offers greater flexibility than systems based on a single biometric. For example, if an illness changes an individual’s voice, the system can rely on other features such as facial recognition and mobile access QR authentication.
  • User Acceptance: Multimodal authentication is more accepted by users as they appreciate having more than one choice to authenticate themselves.
  • Double-locking Security: Combining multiple biometric identifiers for authentication boosts security.
  • Touch-free: In the current pandemic, on-device fingerprint authentication conflicts with society’s increased focus on hygiene. This makes multimodal, touchless biometrics solutions like mobile access QR authentication more attractive than the use of access pads and other shared devices for access control.
  • Convenience: Touch sensors are now ubiquitous, making secure authentication more convenient.
  • Time-saving: Replacing PIN authentication with mobile access QR authentication in smartphones helps save consumers over 40 minutes per week and nearly three hours per month.

Conclusion

Verifying a user’s identity through multimodal authentication for biometrics is growing in popularity—and with good reason, considering the advantages in terms of security, convenience, and hygiene. The key to success lies in creating or leveraging a platform that gathers enough multimodal authentication characteristics to verify an individual’s identity without bogging down the device or its user with several authentication steps.

As a physical identity software solutions provider, Oloid provides secure and privacy-forward multimodal authentication solutions for the modern workplace. We help clients upgrade their antiquated systems by retrofitting them with modern solutions. We enable identity-based, single sign-on for all physical access and authentication needs at the workplace. Connect with us today for a free, no-obligation demo!