What is Liveness Detection?

The term “liveness detection” describes the procedure applied to verify the legitimacy of a user verifying their identity.

Its main goal is to ensure the data being presented is from an actual live person and not a fake identity. It protects against potential risks such as spoofing attacks or presentation attacks.

Liveness detection acts as an additional layer of security and is crucial for improving the effectiveness of biometric authentication systems, especially facial recognition systems. 

Why is liveness detection important?

  • Without liveness detection, attackers can easily trick a biometric system with high-quality photos or videos of authorized users’ biometric traits. Hence, face liveness detection is a crucial tool in fraud prevention within biometric systems.
  • Liveness detection helps in presentation attack detection and prevents spoof attacks.
  • The integration of liveness detection enhances the security of biometric systems by introducing an additional layer of protection. 

Types of liveness detection

There are two types of liveness check systems

Active liveness detection 

Active liveness verification methods require users to engage in specific actions or gestures to validate their physical presence during the digital identity verification process.

Passive liveness detection

Passive liveness detection doesn’t involve the user’s collaboration. It operates by analyzing visual and biometric characteristics present in the image/video.

How does liveness detection work?

Active liveness detection

Active liveness checks necessitate user interaction, typically involving actions or gestures to confirm their physical presence during identity verification. Each liveness detection system has its methods to ensure the user’s authenticity. There are:

  • Requests the users to audibly recite text displayed on the screen. 
  • Users are asked to hold a piece of paper containing specific verification text
  • Requesting the users to perform gestures such as nodding or shaking the head, turning it from side to side, blinking, or other predefined actions

Passive liveness detection

In the context of passive liveness detection, the procedure varies depending on whether an image or a micro video is being analyzed. 

When it is single-image passive liveness detection,

Individuals present themselves in front of a camera, and the image is then processed for feature extraction. The resulting biometric template contains distinct features used by a classifier to determine if the sample is a live human or a fake representation. Genuine samples proceed for identification while fake ones are discarded.

Read more: How to detect AI-generated selfies?

When it is video-based passive liveness detection,

Users present themselves to the camera, and a micro video is recorded. From this video, a series of images are extracted and evaluated individually using neural network architectures, which make decisions autonomously. This process helps identify whether the user is a real person or an impostor.

How does liveness detection API work?

HyperVerge offers single image passive liveness detection API. With liveness detection and deep learning, a photo or video submitted as part of the verification process is rigorously analyzed to confirm that the source is indeed a live human being, not a spoof.

Here’s a step-by-step approach to how the API works:

  1. API integration: Using the unique API keys provided, begin by integrating the liveness detection API into your system or application.
  2. Data Input and Request: Input required data such as the facial image or video. Once this data is provided, the system can initiate a liveness detection request to the Liveness Detection API.
  3. Analysis: The API processes the provided data, analyzing various facial features and movements to determine liveness.
  4. Authentication: Using advanced algorithms and machine learning models, the API verifies the authenticity of the facial data, ensuring that it represents a live person rather than a static image or video playback.
  5. Verification Result: Based on the analysis, the API generates a verification result indicating whether the provided facial data passes the liveness check or if any anomalies are detected along with the confidence score.
  6. Response: The API sends back the verification result to the requesting system in a specified format such as JSON, XML, or plain text, as per the API specifications.
  7. Action: Depending on the result, the requesting system or application can proceed with appropriate actions. For instance, if liveness is confirmed, access can be granted, whereas if suspicious activity is detected, further verification steps may be necessary, such as manual review or denial of access

Read more: Facial Recognition API Vs. SDK: The Right Choice For Liveness Verification

Challenges with existing solutions

  • Poor accuracy in facial matching leads to higher rates of manual reviews, increased false rejection and acceptance rates, as well as elevated operational costs. 
  • AI models lacking training in diverse facial variations may struggle to detect subtle changes, resulting in diminished accuracy. 
  • Implementing active liveness checks, which request users to record short videos, often leading to user drop-offs. 
  • Additionally, relying on outsourced solutions may introduce dependencies and hinder responsiveness to emerging fraud tactics due to slower update cycles.
Input and output of Hypererge's Liveness Detection API

Features of HyperVerge’s Liveness Detection API

Here’s how HyperVerge’s Liveness Detection API can make the process seamless.

Face Match: The Face Match module encompasses ID-to-selfie comparison and image quality assessments. HyperVerge’s exclusive software boasts the highest accuracy in face matching and liveness detection within the industry.

Passive Liveness: Passive Liveness technology ensures precise detection without requiring the user to make intricate gestures, using only a single selfie.

Face Quality Checks: Face Quality Checks offer real-time identification of various factors such as blur, masked faces, presence of multiple faces, closed eyes, and absence of a face, among others.

Face matching checks

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