What Is Multimodal Biometrics & How Does It Strengthen Security?

Multimodal biometrics provides an extra layer of security and maximum flexibility in designing cybersecurity and identity and access management frameworks. securing user identities in biometric authentication techniques.

If you want to understand the concept of multimodal biometrics for user authentication and its potential benefits for your organization, Anonybit’s identity management platform is a valuable asset in navigating this complex landscape. This blog provides an overview of how to implement multimodal biometrics safely.

What Are Multimodal Biometrics?

When a given solution offers multiple biometric scanning options, it is called multimodal. With such a broad definition, the term multimodal biometrics solution can refer to any implementation combining different biometrics types, either to work together as a multifactor solution or to allow many authentication options. 

Combining Technologies for Stronger Authentication

Software that combines face and voice recognition, smartphones that offer access control via fingerprint or iris, and futureproof biometric platforms ready to support and manage any single or multi-factor type of authentication—these all fall under the umbrella of multimodal.

Enhanced Security, Accuracy, and Accessibility

Several benefits are inherent to multimodal biometrics, the most prominent being heightened security and accuracy and greater accessibility/flexibility. For instance, a banking app that uses both voice and face recognition to authenticate user login is more secure than either one of its used modalities alone (since the user’s voice and face must match the stored templates). Providing user choice in modality, such as palm in-store and face online, is another example of how multimodal biometrics can enhance accessibility.

How Does Multimodal Biometrics Work?

Multimodal biometrics leverage multiple forms of biometric authentication to identify individuals. By combining various tough-to-spoof forms of identity verification, firms can create a more robust approach to account security. 

Balancing Security and Convenience

Multimodal biometrics may incorporate any number of different biometric characteristics. For example, a mobile app that asks a user to provide a voice sample and a selfie may be considered too cumbersome for day-to-day use, like checking a bank balance, but may be well regarded as voice biometrics and facial recognition to improve security, negatively impacting the customer experience. Organizations should partner with a reputable vendor that offers a privacy-enhancing biometric solution that can support enterprise-level needs to launch biometric authentication capabilities successfully. 

Frictionless Enrollment and Authentication

Biometric authentication will be deployed to customers during account enrollment. After enrolling in their biometrics, when the account holder logs in, they will be prompted to take a selfie or record a passphrase, for example, and those items will be compared to their biometric template on file.

Achieving Security and Privacy Harmony

At Anonybit, our decentralized biometrics system design helps companies prevent data breaches and account takeover fraud. With a decentralized biometrics solution, companies can enable passwordless login, wire verification, step-up authentication, and help desk authentication. We aim to protect companies from data breaches, account takeovers, and synthetic identity fraud.

To achieve this goal, we offer security solutions such as:

• Secure storage of biometrics and PII data
• Support for the entire user lifecycle
• 1:1 biometric authentication and 1:N biometric matching to prevent duplicates, synthetics, and blocklisted identities 

Anonybit eliminates the tradeoffs between privacy and security. Prevent data breaches, enable strong authentication to eliminate account takeovers, and enhance the user experience across the enterprise using Anonybit. 

Book a free demo today to learn more about our integrated identity management platform.

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Biometric Modalities Used In Authentication

Various modalities are used to ensure secure and accurate user identification. Multimodal biometric authentication combines two or more modalities to enhance security and overcome the limitations of individual biometric modalities. 

Fingerprint Recognition

Fingerprint recognition, including palm prints, involves the unique pattern of ridges and valleys on our fingertips. This distinctive identifier is highly accurate, but challenges may arise from poor-quality images.

Face Recognition

Facial recognition technology uses multiple images stored on personal devices or databases. The system analyzes images and compares them to stored ones. Despite its popularity, facial recognition can be affected by changes in:

• Lighting
• Aging
• Capture quality

Iris Recognition

Iris recognition leverages the intricate patterns in the iris for high-precision identification. Obtaining high-quality images requires special cameras and may present challenges depending on the environment.

Voice Recognition

Voice recognition systems record users’ voices to create biometric templates for authentication. While convenient, voice biometrics can be affected by noise or changes in the user’s voice.

Multimodal biometric authentication combines various biometric modalities to achieve higher security and accuracy. Each modality has its own challenges and limitations that must be considered when implementing these systems.

How Multimodal Biometrics Address Cybersecurity Challenges 

Lower False Rejection Rate

Multimodal biometric security systems may mitigate some of the accuracy issues surrounding biometrics by combining or fusing the scores of the different modalities. This may minimize the False Acceptance Rate.

A False Acceptance Rate (FAR) occurs when a person is accepted/recognized within a system when they shouldn’t be, and a False Rejection Rate occurs when a person is rejected/not recognized by the system when they should be.

Two Locks Are More Secure Than One

By definition, multimodal biometric systems are more resistant to spoofing attacks, as more barriers are put in front of fraudsters who have to work in real time. 

Flexible and Compensating

The system is also much more flexible for users than unimodal biometrics, as it’s easier to combat noise in the data. If a voice is altered by an illness or a fingerprint is altered by an injury, the other biometric trait measured can be used to compensate.

Redundancy

In case one biometric modality fails or is compromised, multimodal systems offer redundancy by using other biometric modalities for authentication. This ensures continuous and reliable authentication even in the event of a failure.

What Are The Disadvantages Of Multimodal Biometrics Systems?

High Equipment Costs

Multimodal biometric recognition technology may require multiple sensors or equipment to collect and analyze biometric characteristics, resulting in high equipment costs and increased system construction and maintenance costs. Depending on the risk profile of the implementation, the investment can pay off by reducing costs related to password resets, fraud, and possible data breaches. 

Algorithm Complexity Is High

Multimodal biometric identification technology requires comprehensive analysis and knowledge of fusing and reconciling multiple biometric scoring mechanisms. This may introduce high costs and complexity around computing resources and technical support. Despite the high level of complexity, this approach offers enhanced security and accuracy in biometric authentication.

Privacy Protection Can Be Difficult

Multimodal biometric technology increases the stakes around collecting and storing biometric data, which is inherently at risk of leakage and abuse of personal privacy information. To address this challenge, organizations must strengthen the protection and management of data to ensure the privacy and security of individuals’ biometric information. New privacy-enhancing techniques like multi-party computation and zero knowledge proofs are designed to address this issue.

Applications Of Multimodal Biometrics

Multimodal biometrics, which combines multiple biometric modalities for identity verification, has various applications across various industries. Some of the key applications of multimodal biometrics include:

Border Control And Immigration

Multimodal biometrics are widely used in border control and immigration systems to accurately verify the identity of individuals entering or leaving a country. Combining biometric modalities such as:

• Facial recognition
• Iris scanning
• Fingerprint recognition

Access Control And Physical Security

Multimodal biometrics for access control to restrict entry to secure areas in facilities such as:

• Office buildings
• Data centers
• Government institutions

By combining multiple biometric modalities, access control systems can provide higher security and prevent unauthorized access.

Financial Services

In the financial services industry, multimodal biometrics are used for identity verification in:

• Account Access
• ATM Transactions
• Online Banking
• In-store payments

Financial institutions can enhance security and prevent fraudulent activities by combining biometric modalities such as:

• Fingerprint
• Voice
• Facial recognition
• Palm vein

Healthcare

Multimodal biometrics are used in healthcare settings to verify the identity of patients and medical staff, secure access to electronic health records, and prevent medical identity theft. Combining biometric modalities ensures accurate identification and enhances patient safety; these are:

• Palm vein scanning
• Facial recognition
• Voice recognition

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Future Trends and Emerging Technologies

One challenge facing even robust identity-authentication solutions like multimodal biometrics is bias. 

• Algorithms aren’t biased, but humans who control the data that goes into the machines can introduce bias into the training process. 
• A facial recognition system trained on images with demographics that aren’t proportional to the actual population can have trouble matching the faces of underrepresented demographics. 

As biometrics become increasingly used and more diversity is captured and included in the databases available, bias becomes less of an obstacle. All of these factors may lead to:

• Higher error rates
• Slower response times 
• An increase in false positives

The good news is that tremendous advancements have been made with leading facial recognition algorithms to address gender and racial bias and the leading performers have closed the gaps in this area.

Generative AI and Liveness Detection

The rise of generative AI has introduced new challenges to biometric authentication systems, particularly in facial recognition. Advanced AI models can now create highly realistic synthetic images and videos, known as deepfakes, which significantly threaten traditional biometric systems.

Liveness detection has become a crucial component in modern biometric authentication solutions to counter this. Liveness detection aims to determine whether the biometric data presented to the system comes from a live, present person rather than a synthetic or spoofed source.

Key aspects of liveness detection:

• Active vs. Passive: Active liveness detection requires user interaction (e.g., blinking, turning head), while passive methods analyze the input without user prompts.
• Multi-factor analysis: Advanced liveness detection systems examine multiple factors such as texture analysis, motion detection, and depth perception.
• AI-powered detection: Machine learning models are trained to distinguish between real and fake biometric inputs, constantly evolving to detect new spoofing techniques.
• Integration with multimodal biometrics: Combining liveness detection with multiple biometric modalities further enhances security and reduces the risk of spoofing attacks.

As generative AI continues to advance, liveness detection technologies must evolve in tandem to ensure the integrity and reliability of biometric authentication systems. This ongoing arms race between security measures and potential exploits underscores the importance of continuous innovation in biometrics.

Building Better Biometrics

The future of biometrics involves rethinking how model developers train their algorithms. Organizations building biometric authentication tools must ensure they train algorithms in various environments and with training data sets that best approximate real-world conditions. 

Enhanced Security, Accuracy, and User Convenience

Incorporating multimodal biometrics improves identity authentication accuracy and security by reducing false acceptance and rejection rates. Thanks to their complexity, multimodal systems are also more resilient to spoof attacks. They add user convenience by reducing reliance on a single modality and circumventing issues like non-enrollment.

Most importantly, multimodal biometrics illustrate how the future of identity verification can be built through decentralized identities and continuous authentication. 

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Book A Free Demo To Learn More About Our Integrated Identity Management Platform

At Anonybit, we help companies prevent data breaches and account takeover fraud with our decentralized biometrics technology. With our decentralized biometrics framework, companies can enable passwordless login, wire verification, step-up authentication, help desk authentication, and more. 

Comprehensive Security Solutions for Companies

We aim to protect companies from data breaches, account takeovers, synthetic identity on the rise, privacy regulations, and digital transformation. To achieve this goal, we offer security solutions such as:

• Secure storage of biometrics and PII data
• Support for the entire user lifecycle
• 1:1 authentication and 1:N matching for lookups and deduplication

Balancing Privacy and Security with Anonybit’s Integrated Platform

Anonybit eliminates the tradeoffs between privacy and security. Prevent data breaches, reduce account takeover fraud, and enhance the user experience across the enterprise using Anonybit. Book a free demo today to learn more about our integrated identity management platform.