Research: Exploring alternative liveness detection providers for true decentralization at Humanode - Part 1

Humanode’s commitment to decentralization and biometric security necessitates a diverse ecosystem of trusted liveness detection solutions. As a biometric-based network, ensuring that each participant is a unique and living human is paramount to maintaining fairness, preventing Sybil attacks, and upholding the principles of decentralized governance.

Currently, Humanode relies exclusively on FaceTec for its biometric identity verification needs. While FaceTec provides industry-leading liveness detection and anti-spoofing measures, relying on a single provider introduces inherent risks—such as vendor lock-in, potential service disruptions, and a lack of redundancy in critical verification infrastructure. To strengthen resilience and foster a more decentralized approach, it is essential to explore alternative biometric solutions that align with Humanode’s values.

This article investigates alternative providers, comparing their technologies, integration capabilities, certifications, and more. 

1. Introduction

1.1 What is biometric liveness detection?

Biometric liveness detection is a cutting-edge technology designed to verify that a biometric sample—such as a face, fingerprint, or iris—comes from a real, live person rather than a spoofed attempt using photos, videos, deepfakes, or 3D masks (Chakraborty & Das, 2014; Khade et al., 2021; Khairnar at al, 2023; Pan et al. 2008). Unlike traditional biometric authentication, which simply matches a stored template with a presented sample, liveness detection actively ensures that the user is physically present and not an artificial imitation.

This is achieved through various techniques, including:

• Active liveness detection – Users perform specific actions, such as blinking, nodding, or smiling, to confirm their presence.
• Passive liveness detection – The system analyzes subtle cues like skin texture, light reflection, and micro-movements without requiring user interaction.
• Multi-modal approaches – Advanced models combine different biometric signals, such as voice and face, to enhance security.

1.2 Why is liveness detection essential?

In an increasingly digital and decentralized world, the need for fraud-proof identity verification has never been greater. Traditional login methods, such as passwords and two-factor authentication, are prone to breaches, phishing attacks, and identity theft. Even biometrics alone are not foolproof—without liveness detection, hackers can manipulate the system using high-resolution images, pre-recorded videos, or AI-generated deepfakes.

For decentralized ecosystems like Humanode, where biometric identity verification determines participation, liveness detection is mission-critical. Without it, malicious actors could easily game the system, creating multiple fake identities to gain undue influence, disrupt governance, or manipulate economic incentives. By ensuring that every user is uniquely and verifiably human, liveness detection upholds fairness, prevents Sybil attacks, and strengthens the integrity of decentralized networks.

1.3 Why is it cool?

Beyond security, liveness detection represents an exciting convergence of AI, computer vision, and cryptographic privacy. Here’s why it’s not just essential, but also a fascinating technological frontier:

• Battling deepfakes – As AI-generated faces become more convincing, liveness detection is on the frontlines, distinguishing real humans from synthetic impostors.
• Decentralized identity verification – Unlike centralized identity providers, liveness detection allows users to prove their uniqueness without relying on governments or corporations.
• Cryptobiometrics – Privacy-preserving cryptography and innovations like zero-knowledge proofs (ZKPs), etc., can allow users to prove their uniqueness without exposing biometric data, enhancing both security and privacy.
• Frictionless authentication – Liveness detection can verify users instantly, making security seamless and invisible while maintaining strong protection.

In short, biometric liveness detection isn’t just a technical necessity—it’s a key enabler of the next generation of secure, decentralized, and human-centric digital identity systems.

1.4 Key reasons Humanode uses liveness detection

As outlined in our 2020 whitepaper—which consolidated extensive comparative analyses of various biometric modalities and solutions—our findings consistently demonstrated that facial likeness detection surpassed all other methods in terms of accuracy and usability. And the  reasons to use it include. 

1. Preventing Sybil attacksIn traditional systems, individuals can create multiple identities to gain unfair advantages—whether in governance, reputation, or financial incentives. Liveness detection ensures that each Humanode participant is uniquely verified, making it computationally infeasible for an entity to register multiple accounts.
2. Enhancing security against deepfake and AI-based attacksAI-generated deepfakes have become increasingly sophisticated, posing a serious threat to biometric authentication. By integrating advanced liveness detection, Humanode ensures that only real, living humans can access the network, preventing spoofing attempts using photos, videos, masks, or synthetic avatars.
3. Enabling decentralized governanceUnlike proof-of-stake (PoS) or proof-of-work (PoW) systems, where influence is tied to wealth or computational power, Humanode follows a proof-of-biometric-uniqueness (PoBU) model, where every participant has an equal say. Liveness detection guarantees that decision-making power remains with real humans, not bots or deepfake-driven exploits.

4. Fair and equitable participationUnlike traditional financial systems where wealth determines influence, Humanode ensures one-person-one-vote governance. Liveness detection allows all participants to be on equal footing, preventing plutocratic control and reinforcing the network’s core values of decentralization and inclusivity.

The big picture

Humanode’s vision is to create a truly decentralized, Sybil-resistant network where identity is based on proof-of-biometric-uniqueness (PoBU), not financial power. Liveness detection is the technological backbone of this vision—ensuring that every node in the network is a real, unique human and that decentralization remains intact.

2. Motivation

Biometric identity verification, particularly liveness detection, is critical for preventing spoofing and ensuring that only genuine users can access systems. Humanode’s present reliance on FaceTec has delivered robust security and user experience. However, the principles of decentralization and resilience encourage the exploration of multiple providers. This research examines other prominent liveness detection providers that employ various technological approaches, offering potential benefits in accuracy, modularity, and multi-modal authentication.

The motivation behind this exploration is twofold:

1. Risk diversification: Reducing reliance on a single vendor mitigates the risk of centralized failure or supply chain issues.
2. Enhanced decentralization: A more distributed ecosystem aligns with Humanode’s core principles, ensuring that security and innovation are not dependent on a sole provider.

3. Methodology

To conduct a comprehensive comparative analysis of leading liveness detection providers, this study employs a multi-dimensional evaluation framework. The goal is to assess the technological sophistication, security robustness, integration flexibility, and industry standing of various solutions, ensuring they align with Humanode’s principles of decentralization, privacy, and biometric-based identity verification. 

The analysis is structured across several dimensions:

3.1 Company profile and market positioning

Understanding a provider’s background helps contextualize its technological approach and strategic vision. Key factors considered include:

• Country of origin – Regulatory landscape and data privacy implications.
• Primary focus areas – Whether the provider specializes in financial services, government IDs, Web3 identity, or enterprise security.
• Target markets – Consumer-oriented, enterprise-grade, or decentralized identity solutions.

3.2 Liveness detection approach

Liveness detection methods vary widely in sophistication and security. This analysis categorizes providers based on their approach:

• Active liveness detection – Requires user participation, such as blinking, smiling, or moving their head to verify liveliness.
• Passive liveness detection – Works in the background without requiring user interaction, detecting signs of real human presence using AI-driven analysis of skin texture, micro-movements, and light reflections.
• Hybrid approaches – Combines both active and passive detection for enhanced security and fraud resistance.

3.3 Supported biometric modalities

Liveness detection is not limited to facial recognition. This study evaluates providers based on their support for:

• Facial biometrics – Standard in most liveness detection solutions.
• Other modalities – fingerprint, iris, voice, even lip-based recognition.
• Multi-modal biometrics – Advanced systems that incorporate several modalities, such as fingerprint, iris, voice, hand geometry, lip-based recognition for greater security and fraud prevention.

3.4 Certifications and accuracy claims

Security and reliability are paramount, especially in mission-critical applications like decentralized identity verification. This dimension examines:

• Compliance with industry standards – Including ISO/IEC 30107-3, iBeta Presentation Attack Detection (PAD) testing, GDPR, and other regional regulations.
• Accuracy metrics – False Acceptance Rate (FAR), False Rejection Rate (FRR), and anti-spoofing success rates against deepfakes, masks, and digital injections.

3.5 Integration and developer support

This study reviews:

• SDK and API availability – Whether providers offer robust software development kits (SDKs) and APIs for integration with decentralized applications (dApps) and blockchain networks.
• Developer documentation – Clarity, ease of use, and the presence of open-source components or sandbox testing environments.
• Customizability – The ability to tailor solutions to Humanode’s unique proof-of-human-uniqueness model.

3.6 Recent developments and industry trends

The liveness detection space is evolving rapidly, with new advancements in AI, deepfake detection, and cryptographic privacy techniques. This study tracks:

• Notable product updates – Major feature rollouts and AI advancements in anti-spoofing technology.
• Strategic partnerships – Collaborations with governments, financial institutions, or Web3 identity projects.
• Mergers and acquisitions – Consolidation trends that may impact market dynamics and availability of solutions.

By assessing these dimensions, this research aims to provide a holistic evaluation of the current liveness detection landscape, identifying alternative providers that align with Humanode’s mission.

4. Results of comparative analysis

4.1 Liveness detection methodologies

A key differentiator among biometric providers is their liveness detection strategy:

• Active detection: Several companies—such as IDnow and TECH5—rely on active liveness detection, where the user is prompted to perform an action (e.g., blinking, head movements) to confirm authenticity. This approach reduces the risk of spoofing via static images.
• Passive detection: Providers like BioID, iProov, and Veriff emphasize passive detection, wherein the technology analyzes subtle cues such as facial skin texture, depth, and micro-movements in a single image or video stream. For example, iProov’s Genuine Presence Assurance uses both passive algorithms and real-time challenges.
• Hybrid systems: Some companies, including FaceTec and Oz Forensics, combine both active and passive elements. FaceTec’s patented 3D FaceMaps™ and integrated challenge–response mechanisms illustrate the evolving trend toward multi-layered verification, which can address a wide range of spoofing attempts from printed photos to deepfakes.

4.2 Modalities in biometric authentication

While face recognition dominates the market, many companies have expanded to include additional biometric modalities:

• Facial liveness detection: Nearly all surveyed companies support facial recognition. Innovations range from 2D image analysis to advanced 3D mapping techniques (e.g., FaceTec and LIPS Corporation).
• Multi-modal systems: Companies like IDENTY and ID R&D extend beyond facial biometrics by incorporating fingerprints, palm prints, and voice. This multi-factor approach enhances security by combining complementary data points. 
• Specialized modalities: Some firms target niche applications; for instance, Nymi integrates fingerprint scanning with heartbeat verification on a wearable device. Lip movement analysis, pioneered LIPS Corporation, authenticates users based on the unique motion of their lips during speech.

4.3 Integration and developer support

Ease of integration and comprehensive developer documentation are critical for widespread adoption:

• Robust SDKs and APIs: Many companies, including iProov, Innovatrics, and Shufti Pro, offer extensive API documentation and downloadable SDKs for multiple platforms (iOS, Android, web). This developer-centric approach reduces time-to-market and enables seamless integration into existing systems.
• Modular solutions: Some providers, such as ALiCE Biometrics and Chooch AI, present modular solutions that allow companies to tailor integrations based on their specific requirements. The flexibility to deploy on-premises or in the cloud is a recurring theme.
• Lightweight SDKs: Notably, Saffe’s offering emphasizes a very small SDK footprint (e.g., 400 KB for mobile platforms), which is particularly advantageous for applications targeting lower-end devices or where rapid performance is essential.

4.4 Certifications and accuracy

In a market where security is paramount, industry certifications and performance metrics serve as important trust signals:

• ISO/IEC 30107 and iBeta compliance: A significant number of companies (e.g., FaceTec, IDEMIA, and Oz Forensics) boast compliance with internationally recognized standards for presentation attack detection. This compliance assures clients that the systems have undergone rigorous testing.
• Accuracy metrics: High accuracy claims—such as FaceTec’s “1-in-12.8 million” false acceptance rate and Oz Forensics’ 99.87% accuracy—highlight the level of precision required for high-stakes environments such as financial services and border security.
• Patent and spoof bounty programs: Initiatives like FaceTec’s patented 3D FaceMaps and spoof bounty program further differentiate providers by incentivizing the identification and remediation of vulnerabilities.
• For Humanode, an additional challenge is 1:N search and matching—the ability to check for duplicates across a vast biometric database. Only a few providers meet this requirement, including BioID, IDENTY, Oz Forensics, and Paravision, making them prime candidates for further evaluation.

4.5 Market trends and partnerships

Beyond technology, companies are increasingly active in forging strategic partnerships and expanding into new markets:

• Geographic diversity and niche focus: Companies are emerging from across the globe—from the UK (Saffe, Yoti) and USA (Jumio, iProov) to emerging hubs in Taiwan (LIPS Corporation) and Australia (BixeLab). This geographic diversity reflects the global demand for secure, reliable biometric systems.
• Evolving use cases: The ongoing evolution of use cases—from secure banking and payment systems to digital health passes and remote voting—drives the continuous innovation observed in liveness detection and biometric matching technologies.
• Mergers, acquisitions, and collaborations: Recent news updates indicate active market consolidation and strategic partnerships with major players like Microsoft, which seek to integrate biometric solutions into broader digital identity frameworks.

5. Liveness detection providers: Landscape 2025

The following table summarizes key aspects of researched liveness detection providers and serves as a baseline for exploring alternative technologies that could be integrated into a Humanode decentralized biometric verification system.

Note: This table is a condensed overview drawn from detailed data. For full technical and market details, please refer to each company’s profle and documantation.

Note: The analysis is based on aggregated company data and recent market news as provided in the comparative dataset. For further details on individual companies and technical specifications, readers are encouraged to consult the respective developer documentation and industry press releases.

6. Conclusion

True decentralization demands a diversified provider strategy—one that minimizes single points of failure, enhances redundancy, and encourages continuous innovation through competitive integration. 

For Humanode, the search for alternative liveness detection providers is all about reinforcing decentralization, fortifying system resilience, and future-proofing biometric integrity. Each contender offers a unique technological edge, a distinct approach to integration, and a specialized market presence, making a nuanced evaluation essential.

A particularly complex challenge lies in 1:N biometric search and matching, a cornerstone of Humanode’s identity framework. Only a handful of providers—BioID, IDENTY, Oz Forensics, and Paravision—have the capability to meet this demand, positioning them as primary candidates for deeper scrutiny.

This analysis is just the beginning. Moving forward, rigorous testing, pilot integrations, and security assessments will define which solutions align with Humanode’s uncompromising standards. The goal is clear: to build a biometric verification ecosystem that is not only decentralized but also robust, adaptable, and resilient against the evolving landscape of digital identity.

References

• Chakraborty, S., & Das, D. (2014). An overview of face liveness detection. arXiv preprint arXiv:1405.2227.
• Khade, S., Ahirrao, S., Phansalkar, S., Kotecha, K., Gite, S., & Thepade, S. D. (2021). Iris liveness detection for biometric authentication: A systematic literature review and future directions. Inventions, 6(4), 65.
• Khairnar, S., Gite, S., Kotecha, K., & Thepade, S. D. (2023). Face liveness detection using artificial intelligence techniques: A systematic literature review and future directions. Big Data and Cognitive Computing, 7(1), 37.
• Pan, G., Wu, Z., & Sun, L. (2008). Liveness detection for face recognition. Recent advances in face recognition, 109-124.