Active Liveness vs. Passive Liveness Key Differences and How They WorkAuthor: Luke Oliver | 06 Oct 2023
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In identity verification and biometrics, the choice between “Active Liveness” and “Passive Liveness” has become a pivotal consideration. Active and passive liveness are two types of liveness detection techniques that ensure that the person behind the camera is an actual individual. Let’s first find out how liveness detection works!
The active vs passive decision carries significant implications for both security and user experience. In this article, we dive deep into the specific categories, weighing their strengths, weaknesses, and real-world applications.
An Overview of Liveness Detection Technology
Liveness detection or face liveness is an integral part of advanced facial recognition systems that detect real individuals from spoofing attempts. In an era of digital innovation, biometric systems such as facial recognition have become the standard in biometric authentication. Liveness Detection is considered a key element in order to prevent cases of identity theft, especially because fraudsters now use sophisticated methods to commit fraudulent activities.
Facial Liveness detection is not just about face matching; it’s about ensuring that the face, or any other biometric feature presented, is live and genuine. Hackers and fraudsters nowadays make use of advanced facial masks or deepfake technology to try and spoof the system. The competence of these systems depicts whether they allow any of such advanced attempts to pass through or not.
Why Biometric Liveness Detection is Important?
For businesses, the significance of liveness detection is two-fold. Firstly, it acts as a shield against identity theft detection, ensuring that your clients and their data remain protected. Secondly, in an age governed by stringent regulations like GDPR, businesses need solutions like 3D liveness detection to ensure compliance.
With fraud detection becoming a critical element in the overall process, it is no longer just about verifying an identity, but about confirming the ‘liveness’ of that identity. This, in turn, enhances the user experience, increases trust and eventually helps businesses decrease costs.
What is Active Liveness Detection and How Does it Work?
Active Liveness Detection
Active liveness detection, as the name suggests, requires some sort of activity from the user. If a system is unable to detect liveness, it will ask the user to perform some specific actions such as nodding, blinking or any other facial movement. This allows the system to detect natural movements and separate it from a system trying to mimic a human being.
It is an advanced approach as compared to traditional verification that works on a frame-by-frame, image-based model. Industry leaders, such as facia, provide a blend of active and passive liveness, where active is only initiated when there is not enough evidence from frame-by-frame images.
How Does Active Liveness Work?
Active liveness detection operates with precision and attentiveness, crucial for robust biometric authentication. It captures multiple images (at least two) of the user in motion, often requiring facial movements. Advanced artificial intelligence (AI) capabilities analyse and confirm the identity of a real person.
This method provides protection against various presentation attacks, including the use of 3D masks and other biometric spoofing attempts. It plays a pivotal role in the verification process, establishing and safeguarding digital identities, and ensuring that only live individuals, not impostors, gain access through biometric checks.
Benefits of Active Liveness Detection
Active liveness demands user interaction, ensuring robust protection against spoofing attacks. It verifies the genuine presence of the user during the verification process, providing a high level of security.
Ideal for scenarios governed by data protection regulations like GDPR, KYC, and AML, where user engagement and consent are critical.
The very design of active liveness acts as a deterrent to fraudsters, making spoofing a challenging ask
Challenges of Active Liveness Detection
Active liveness requires user actions like smiling, blinking, or nodding, which can occasionally inconvenience users and result in dropouts from the verification process. Sometimes, the individual may not be able to read or understand the instructions to perform gestures.
Implementing active liveness can be more complex than passive methods, often necessitating specific hardware or software integration, which may add complexity to the system setup. It also requires advanced system requirements and has high associated costs.
The active liveness process can be slower when compared to passive detection methods. This may have an impact on the overall user experience, especially in situations where speed is crucial.
Real-World Applications of Active Liveness Detection
Active liveness is widely used in the financial services industry to ensure secure customer onboarding, particularly in online banking and digital transactions.
In the healthcare sector, active liveness plays a crucial role in upholding patient identification integrity and securing electronic health records.
Government and Law Enforcement
Government and Law Enforcement agencies rely on active liveness for verifying identities during border control processes and citizen services, increasing national security and data protection efforts on a mass level
What is Passive Liveness Detection and How Does it Work?
Passive Liveness Detection
Passive liveness detection operates discreetly in the background, requiring no explicit action from the user. The system’s artificial intelligence continuously analyses facial movements, depth, texture, and other biometric indicators to detect an individual’s liveness.
While passive liveness checks offer a smoother user interaction, it’s essential for businesses to be transparent about these passive checks, ensuring users are aware and any privacy concerns are addressed.
How Passive Liveness Works
Passive liveness detection revolves around intelligent observation. By analysing a single captured image, the system identifies genuine human indicators and contrasts them against known spoofing tactics, ensuring that only legitimate users gain access. These indicators include skin texture, depth, 3D face mapping and environmental factors.
Strength of Passive Liveness Detection
Offers swift and smooth verifications, enhancing the user experience as it does not require specific user actions. The individual only needs to put his face in front of the camera and the system does the rest.
More efficient and faster than active liveness, leading to higher conversion rates.
Works well in various scenarios where speed and ease of use are prioritised. Requires a lower criteria in terms of integration than active liveness.
Challenges of Passive Liveness Detection
In high-risk situations and complex environments, passive liveness detection may perform to limited capabilities.
Users may not be aware of image capture during passive detection, which can raise valid privacy concerns that businesses must address proactively.
Lack of Engagement
Since passive detection does not require user actions, it may not be suitable for scenarios where user engagement is deemed crucial for security and fraud prevention.
Real-World Applications of Passive Liveness Detection
E-commerce and Retail
Passive liveness detection is instrumental in countering fraud in online shopping, preventing account takeovers, and mitigating identity theft.
It plays a pivotal role in ensuring secure user verification during SIM activations and account registrations in the telecommunication sector.
Passive liveness detection significantly enhances the user experience across various industries, particularly in situations where speed is essential for successful onboarding and user engagement.
Active Checks VS Passive Liveness Checks?
Active and passive checks differ mainly in user engagement and the technology they depend on. Active checks need the user to interact, while passive checks work silently. When it comes to capturing images, active checks often use multiple frames, but passive checks might only need one.
Active Liveness Checks
- Ask for user actions, making it tough for attackers to use photos or videos to trick the system
- Mix movement study and AI, often looking at several images
- Need the user to be alert, fitting for services that put privacy first
- Can add challenge-response tasks, boosting security against deep fakes
- Best for services that focus on tight data safety
Passive Liveness Checks
- Don’t ask the user to do anything specific
- Mainly use AI, often looking at just one picture
- Work without the user knowing, usually in the background
- Can be faster and easier for the user, needing no special actions
- Great for services that want to make things easy for users
Role of SDKs and APIs in Liveness
For anyone belonging to the tech world, it is relatively an easier distinction. However, if you are becoming familiar with liveness detection technology, it is essential to understand how they can be deployed onto your systems.
For mobile-based integration, facial recognition vendors provide Software Development Kits (SDKs). These SDKs are made compatible with both iOS and Android devices. Moreover, leading providers of face recognition services have robust systems that can work on low-resolution devices as well.
Let’s explore further specifications for SDKs and APIs respectively.
Software Development Kits (SDKs)
Face Liveness Detection SDK
- Certified by iBeta, compliant with ISO 30107-3 standards
- Requires a live video stream; the liveness check is performed before feature extraction
- Only one face should be visible in the frame
- Minimum 1280 x 720 pixels video stream resolution for ISO compliance
- 5 fps or better; works with colour and grayscale images. (active)
- Requires color images and 10 fps. (passive)
For organizations aiming for a coherent brand experience, Facia offers white-label SDKs:
- Custom Branding: Instead of showcasing Facia’s branding, the SDK mirrors the client’s unique branding elements, ensuring a unified user experience
- Ownership: Organizations have complete ownership of the white-labeled SDK, setting it apart from standard vendor-branded solutions
- Cost Consideration: While white labelling involves an added cost due to the bespoke branding and customization, the investment enhances brand identity and user trust
For functionalities that transcend mobile platforms, web-based solutions become indispensable. Facia understands this need:
- Bespoke Solutions: Facia crafts robust Restful APIs, which can be tailored to align with the intricate requirements of a business.
- Liveness Detection Flexibility: Facia’s APIs offer the flexibility to embed either active liveness detection, passive liveness detection, or a fusion of both. This adaptability ensures that businesses can strike the right balance between security rigour and user experience
In identity verification, the choice between active and passive liveness detection ultimately depends on your requirements. High-profile businesses with sensitive information such as banks and financial institutions have to rely on active liveness detection. However, companies offering retail and e-commerce solutions can also work with passive liveness detection.
Facia, with its state-of-the-art solutions, stands at the forefront of liveness detection technology. It offers both active and passive liveness detection solutions alongside customisable options for branding and integration. Facia has the ability to provide on-cloud integration as well as on-premise integration.
However, in both cases, the priority is data protection and privacy. Businesses can deploy the solution onto their designated servers, and reserve the rights to their customers’ data.
Choose Facia’s Liveness Detection Solution
Facia stands at the forefront of biometric technology, offering both SDKs and APIs tailored for active or passive liveness detection. As security challenges evolve, partnering with Facia ensures that organizations remain a step ahead, safeguarding their operations and users with unparalleled biometric verification solutions.
With a focus on user experience, security, and compliance, Facia ensures a seamless and secure verification process.
Facia Is The World’s Fastest Liveness Detection Solution Provider:
- <1s Response Time
- Passive & Active Liveness Detection
- 0% FAR @ <1% FRR
- Compatible SDKs For iOS & Android
- Protection Against Injection Attacks
- iBeta Level 1 Certified
Frequently Asked Questions
Facial liveness detection is a vital component of biometric facial recognition systems. It ensures that users are live and physically present during the biometric verification process, preventing fraud attempts like photo spoofing, mask attacks, and video replays. While facial recognition verifies identity, liveness detection confirms the user's real-time presence.
Facial anti-spoofing refers to the set of techniques and technologies used to detect and prevent spoofing attempts in facial recognition systems. Spoofing attacks involve fraudulent attempts to trick the system into recognizing an unauthorized user as a legitimate one.
Presentation Attack Detection (PAD) is a crucial security measure in biometric systems. It identifies and prevents various presentation attacks, such as photo spoofing, 3D masks, or video replays, ensuring the authenticity of biometric data.
Facia is a leading provider of Facial Liveness Detection SDK. Our SDK seamlessly integrates into applications and systems, enabling real-time assessment of user liveliness during facial recognition. It offers robust security against spoofing attacks, making it an excellent choice for businesses prioritizing authentication security.
A Liveness Detection API is a programming interface that allows developers to incorporate liveness detection functionality into their applications or services. Facia's Liveness Detection API offers a convenient way to implement real-time liveness checks, enhancing the security of biometric authentication systems.