What is Liveness Detection and How Does It Work?

In a recent incident, a prominent Indonesian financial institution reported a deepfake fraud incident impacting its mobile applications. Group-IB’s threat intelligence identified over 1,100 deepfake fraud attempts, where AI-generated deepfake photos were used to bypass the institute’s digital Know Your Customer (KYC) process for loan applications. Cybercriminals used deepfake technology to bypass facial recognition systems, highlighting the alarming vulnerability of digital security. The growing need for advanced security solutions, such as facial liveness detection, is driving the global biometrics market, which is expected to reach $104.22 billion in 2029 (Mordor Intelligence Inc). 

As digital threats continue to evolve, liveness detection is crucial for safeguarding sensitive data and protecting against fraud, particularly in ID verification processes across multiple sectors.

Key Takeaways

• Liveness detection secures biometric systems against fake photos, silicon masks, and other presentation attacks.
• Liveness detection strengthens the digital identity verification process.
• Advanced 3D liveness detection combats even sophisticated deepfake attacks on identity verification systems.
• Liveness detection strengthens security by defending against fraudulent activities like account takeover and unauthorized access, safeguarding against the manoeuvres of fraudsters.
• Facia offers the fastest liveness detection solution, powered by both a liveness SDK and a liveness API.

This blog will discuss the essence of liveness detection technology: what it is, how it works, and its pivotal role in preventing spoofing and presentation attacks.

What is Liveness Detection?

Liveness detection or liveness check, is a security measure that verifies the presence of a live human being when presenting biometric data such as a face or a fingerprint, rather than a fake image or a recording. 

In essence, liveness detection acts as a digital gatekeeper, identifying fraudsters, attempting to use copied or forged biometric data to unlawfully access a system. 

Generally, liveness detection involves authenticating whether the claimed identity submitted to your systems represents a real person. In facial biometrics, liveness checks verify if a human is physically present (real-time) before a camera, rather than a spoof like, a 3D mask, or image displayed on a screen.

The most sophisticated form of liveness detection is 3D liveness checks, which create a 3D facial map using depth sensing technology. This approach leverages artificial intelligence (AI) and convolutional neural networks (CNN) to distinguish between real people and even deepfakes, ensuring the highest level of security.

Find out the insider’s scoop on liveness verification and how facia differentiates between you and the imposters.

History of Liveness Detection

Early Beginnings (1950s): The idea of confirming a real person interacting with a system started with Alan Turing’s Test. Although it was not specifically about biometrics, it still explored differentiating between humans and machines via conversation, laying the ground for future authentication methods.

Knowledge-based Approaches (1990s): Early authentication used personal questions such as “mother’s maiden name” but this proved easy to bypass and relatively a weak defense against fraud attempts.

Machine Learning Techniques (2000s): The 2000s brought machine learning technology, allowing biometric systems to use facial analysis and motion detection to effectively spot real interactions from fake ones.

The Deepfake Era (Present Day): As deepfakes began to emerge as a widespread threat, liveness detection evolved to address these modern challenges. Pioneering work by experts like Dorothy E. Menning helped shape this evolution. Today, liveness detection incorporates advanced methods such as 3D facial mapping, facial movement analysis, and sophisticated challenge-response techniques (active and passive liveness) to confirm the presence of a real person.

Types of Liveness Detection

Let’s explore different types of Liveness detection methods.

Active Liveness 

Also known as the first generation of liveness detection technology, active liveness requires explicit interaction between the user and the system. The user follows specific instructions provided by the software, such as smiling, blinking or turning their head. It is considered highly effective at identifying spoofing attempts, such as static photos or videos. 

Active liveness is often perceived as more secure due to its step-by-step engagement, making it difficult for deepfakes or other impersonation methods to pass through. However, the need for user cooperation and the extra steps involved may impact the overall user experience, making it less seamless for some users.

Passive Liveness

Passive liveness detection is more streamlined and does not require any specific actions from the user other than simply presenting themselves to the camera, often through taking a selfie. It is considered more user-friendly and convenient, as it doesn’t interrupt the user’s flow with additional instructions, and since it works without user interaction, passive liveness is seen as more seamless and quicker, offering a smoother experience. 

This approach may use advanced algorithms that analyze natural facial movement, eye tracking, and environmental cues to validate liveness without requiring user commands.

Hybrid Liveness

Known as a semi-passive or hybrid form of liveness detection. This method of liveness detection combines the best of both approaches. A combination of active and liveness detection tests such as taking a selfie and performing an active task (physical motion) is the methodology used in a hybrid liveness test.

This method aims at making the verification process smooth for customers but still more secure than completely passive methods.

How Liveness Detection Works?

Liveness detection uses advanced algorithms, powered by artificial intelligence (AI) and machine learning, to analyze facial features in real time. It looks for subtle movements like blinking, and head tilts, and even examines the environment for inconsistencies – all signs of a living person interacting with the system.

By analyzing these elements, liveness detection can differentiate between a real person and a spoof attempt and adds an extra layer of security to prevent fraud and protect online identity. 

Detailed breakdown of how liveness detection works?

Motion-Based Detection

Motion-based detection involves involuntary micro-movements like blinking, eye twitches, or subtle changes in facial expressions to detect signs of life. These movements can be caused by natural physiological processes like respiration and muscle twitches, and are difficult to replicate perfectly in static images or videos.

Texture Analysis

Texture Analysis Analyzes skin texture, pores, and subtle variations in colour and reflection to distinguish real skin from spoofed images. This can involve examining microscopic details like sweat patterns and capillary structures, which are difficult to forge in artificial replicas.

3D Imaging and Depth Analysis

This technology uses specialized depth cameras to capture a three-dimensional image of the face, creating a digital model that maps the face’s shape and depth. This method can easily distinguish between a real, 3D face and a flat image or mask, which would appear two-dimensional in the depth map. Also, 3D imaging can detect inconsistencies in lighting and reflections that might indicate a spoofed image.

AI/ML-Based Detection

Advanced artificial intelligence (AI) and machine learning (ML) algorithms analyze the smallest details in facial features and movements. These algorithms can detect things like subtle variations in expressions, pupil movement, and mouth shape – elements almost impossible to perfectly replicate in a spoofed image or video.

Different Liveness Detection Methods

Liveness detection safeguards identity verification by employing a diverse arsenal of methods. Here’s a closer look at these techniques:

• Facial Analysis: This method scrutinizes the inherent properties of a captured face (image or video) to distinguish a real person from a fake. It analyzes details like skin texture, micro-movements and lighting/reflection.
   
• 3D Checks (Depth Perception): This technique utilizes specialized 3D cameras or depth sensors to create a depth map of the user’s face. Inconsistencies in in-depth information can expose spoofing attempts, offering an extra layer of security.
   
• 3D Face Mapping: This cutting-edge method builds upon 3D checks by creating a precise 3D model of the user’s face. This detailed map captures unique facial contours and geometry, making it extremely difficult to replicate with a fake. 
  • 2D vs. 3D Maps: Liveness checks leverage neural networks to analyze facial maps. These maps can be:
    • 2D (X, Y): Used in passive checks, often requiring just a selfie.
    • 3D (X, Y, Z): A more advanced approach for superior security.
• User Interaction Challenges (Active Liveness): Prompting users for specific actions like blinking, head movements, or reading on-screen challenges to verify physical presence.

These methods often work in conjunction, with passive analysis (facial analysis) happening in the background and more complex checks (3D checks, 3D mapping) triggered only when necessary. This combined approach ensures a seamless user experience during face verification.

Benefits of Liveness Detection

• Faster Verification: Liveness detection, particularly the passive type, allows for instant and document-free identity proofing. This enables secure authentication through facial scans, streamlining online transactions.
• Enhanced Security: Liveness detection strengthens facial recognition systems by adding an extra layer of protection against sophisticated spoofing attempts.
• Identity Theft Protection: Liveness detection helps prevent unauthorized access and identity theft by distinguishing between individuals with similar features. It ensures that only authorized users gain access.
• Fraud Prevention: By confirming a live presence, liveness detection significantly reduces fraud risks and secures sensitive data.
• Presentation Attack Detection: Liveness detection protects biometric security systems from presentation attacks involving photos, videos, or masks, safeguarding biometric systems from presentation attacks.
• Improved User Experience: Liveness detection facilitates swift and reliable authentication, minimizing false positives and building user trust. This translates to a smoother user experience thanks to sophisticated liveness detection algorithms.
• Protection from Deepfakes: Advanced liveness detection techniques can even separate real faces from AI-generated deepfakes, maintaining system integrity.
• Privacy Champion: Liveness detection ensures biometric data is collected with explicit consent and processed securely, often locally on the device, minimizing the risk of unauthorized access or use. This approach helps safeguard user privacy by ensuring sensitive data never leaves the user’s device without proper authorization. 

Final Word

Liveness detection safeguards our digital identities by preventing spoofing attempts. As facial recognition technology evolves, face liveness detection will be further refined with AI and machine learning, offering faster, more accurate verification with an enhanced user experience. The potential integration of multimodal biometrics unlocks even higher security levels. 

From physical access control to online transactions, it paves the way for a more secure and convenient future.