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Liveness Detection | A Key To Anti-Spoofing For Fool-Proof Identity Verification

Liveness Detection | A Key To Anti-Spoofing For Fool-Proof Identity Verification

Author: admin | 23 May 2024

When a person receives an online video call with a client or a family member, there’s a high chance of it being a spoof called a deepfake that intends to hack your credentials and gain access to your bank account. You won’t believe how convincing that video would be having the same face and same voice but you won’t notice due to a busy life. Ignoring these threats, you may fall victim to identity fraud and lose your money or leak your trade secrets thinking of the other person as a family member or another trusted party. Online Identity Fraud has become a nuisance for multiple sectors that operate online. It includes deepfake attacks, morphing, mask attack, and many other vectors that cybercriminals use to commit different crimes. 

To counter identity spoofing, identity verification solution providers play a critical role though they are not authorized to take legal actions against the criminals, they are empowered to identify, detect, and report any suspicious activity including a potential spoofing attempt. However, these IDV solutions can only work with proper equipment and tools.  One of the main features that IDV solutions use to detect anomalies and identity theft attempts is Liveness Detection.

Key Takeaways

  • Facial recognition technology relies on liveness detection to prevent identity spoofing attempts like deepfake attacks.
  • 3D liveness detection enhances the facial identity solution’s performance using AI.
  • Active Liveness and Passive Liveness are the 2 types of Liveness detection that ensure that a living person sitting in front of the camera.
  • Liveness Detection and its benefits raise some concerns, like customer facial data privacy and user consent, that need to be addressed.

What is Liveness Detection?

Liveness Detection is a testing methodology involving biometric recognition technology that determines that the biometric data comes from a living person. Liveness detection is a critical aspect of biometric identity verification especially used in the case of facial recognition, fingerprint scanning, and Iris. Liveness checks are performed to ensure that no identity spoofing attempt such as a deepfake injection attack is trying to bypass identity verification.

Liveness Detection for Facial Recognition 

Facial Recognition greatly relies on liveness detection to prevent spoof attacks of any sort. These attacks can be of multiple types including:

  • Mask Attacks
  • AI Deepfake Attacks
  • Facial Morphing
  • Replay Attacks

Since Facial Recognition has become far superior than ever before, it requires advanced liveness face detection to combat these attacks. 

Face Liveness Detection Process Explained

Liveness Detection | A Key To Anti-Spoofing For Fool-Proof Identity Verification

1. Initial Capture

The facial image or video is captured through high-resolution devices including smartphone selfie cameras and other facial recognition setups. It is further broken down into 2 steps:

  • User Action: The facial recognition solution will prompt the user to take a selfie.
  • System Action: The system will record multiple frames of a user’s face image. It can also record a high-resolution video for video verification.

2. Image Quality Assurance

  • Purpose: It ensures that the image quality suffices the requirements and standards in facial identity verification to prevent low-quality spoofs.
  • Analysis: The facial liveness detection technology ensures that certain specific conditions are met in the captured images to ensure high-level output face images. These include lighting conditions, glare effect, face exposure, and resolution of an image.

3. Liveness Detection Technique

In the third step, Active and Passive Liveness checks are performed to ensure that a real and living person’s face attempts facial liveness verification. We will discuss these techniques in the next section of this blog.

4. Thermal Imaging

If available, thermal imaging captures the heat emitted from a living person’s face, differentiating it from a non-living object like a latex or paper mask. It is considered a part of passive liveness, but it is not commonly used due to cost and technological complexity.

5. Data Processing and Analysis

The system runs various algorithms including AI liveness detection and analyzes the facial images for further processing.  

3D Liveness Detection

Biometric Liveness Detection prefers the use of 3D technology in facial recognition. Three-dimensionality can be applied only in facial liveness verification and Iris or Retina scanning as biometric fingerprint scanning requires a fingerprint that can easily be detected in 2D format as well. However, Iris and Retina scan can also incorporate 3D scanning for better results with the use of AI technology.

3D Liveness Detection enhances the facial recognition solution’s capability, ensuring highly effective and accurate anti-spoofing through 3D liveness checks. These checks detect the three dimensionality of a facial image detecting the realness and face liveness. In normal mobile phone selfie cameras 3D Liveness Detection is not available but in sophisticated facial scanning technologies that are used to verify liveness in sensitive areas, 3D liveness checks are performed to ensure through:

  • 3D depth analysis via infrared scanning will create infrared patterns and contours of the human face.
  • 3D Depth sensing via structures light throw that uses focused light on a human face to detect depth in facial features for example, a nose is nearer to the camera than the ears and it can be detected by throwing a focused light that will determine this distance from the lens.

Types of Liveness Detection

Liveness detection technology is far more complex than it seems. It involves 2 types of Liveness checks:

This infographic is a graphical illustration of the difference between Active Liveness and Passive Liveness Detection.
Active Liveness
  • It prompts the user to perform a few facial gestures and movements in front of the camera.
  • Usually, these gestures are:
    • Tilting head upside down or turning head left or right.
    • Blinking eyes several times.
    • Smiling or frowning.
  • Active Liveness checks determine the liveness of a user through human gestures and can be imitated.
Passive Liveness
  • It performs liveness through sophisticated back-end face recognition liveness technology.
  • It uses AI algorithms to maintain a seamless user journey in facial recognition and liveness detection.
  • It balances user convenience with security with the use of AI.

Read further on Active Liveness vs. Passive Liveness   

Apart from the basic 2 techniques of liveness detection, Liveness detection can also be checked in the following 2 aspects:

These are the two basic techniques of Liveness Detection. 1) Onsite Liveness Detection 2) Offsite Liveness Detection. Both use active and passive liveness detection processes as per the situation.

1. Onsite Liveness Detection

It refers to a liveness verification check conducted on a person in a live video call or an image uploaded live. In other words, it works on the method of live face detection where a person’s latest, live, valid facial image or video is captured there and then through a liveness detection technology in a facial recognition system. It uses both Active Liveness and Passive Liveness checks separately or as a hybrid liveness detection which combines both active and passive liveness checks in a single phase of face liveness detection.

2. Offsite Liveness Detection

Offsite Liveness is a unique way of testing liveness in an already existing picture on the internet for example a social media profile picture or a YouTube video. Suppose there is a pre-recorded video call in which a facial identity verification system needs to identify deepfakes in this video, it will use offsite liveness checking techniques and tools. It may only use passive liveness detection as the picture itself is not from a living person who is present in front of the camera. 

Is Passive Liveness Better?

Industry experts in the digital identity sector are now focusing on improving the algorithms of Passive Liveness checks for the following reasons:

  • Passiveness is much more sophisticated and user-friendly.
  • Passive liveness checks can confirm the realness and fakeness in less time without the need to communicate with users.
  • Passive liveness is fast as it only requires a user to face the camera and automatically verifies the user’s liveness in seconds.

Face Liveness Verification vs. Face Liveness Detection

There is a slight difference between face liveness verification and face liveness detection. One must distinguish the two. 

  • When face liveness is determined through both Active and Passive liveness checks, it is known as liveness detection.
  • Face Liveness verification refers to verifying a person’s identity through face scanning and using liveness detection.
  • In short face liveness detection is a part of face liveness verification.

Advantages of Liveness Detection for Face Recognition

Liveness Detection is a core mechanism of differentiating between a fake identity and a real user. Through liveness checks, facial recognition systems are confident that no facial identity spoofing goes undetected. Liveness Detection delivers the following benefits and supplements facial recognition.

  • Enhanced Biometric Security is the core feature of liveness detection. It detects spoofing attacks and helps in the trust-building of customers in businesses that require facial identification. 
  • Fraud prevention is also strengthened with the implementation of AI-liveness detection as spoofing attacks now use AI to create deepfakes or digital replay attacks to bypass facial recognition. Liveness detection in facial recognition will not let this happen.
  • The operational efficiency is also an advantage that facial identity verification solutions will deliver through liveness detection. There will be a reduced number of false positives and false negatives when a highly accurate and swift liveness check is performed.
  • The user experience is enhanced through passive liveness as it reduces the overall onboarding time through facial identity verification
  • Businesses need to comply with the latest KYC (Know Your Customer) and (AML) Anti-Money Laundering regulations. Liveness Detection fortifies businesses, helps them achieve their regulatory compliance goals, and protects businesses from suspicious activities and criminals.
  • There is a wide array of use cases of liveness detection especially when it is used in selfie cameras. Users can be safely, swiftly, and accurately identified and verified through selfie cameras using liveness checks from anywhere; thus increasing the use cases of facial recognition such as border crossings, e-commerce platforms, financial institutions, etc.
  • Third-party identity solution providers can have a competitive advantage by using facial liveness detection with advanced AI algorithms.


Read More: AI Facial Recognition with Liveness Detection for Border Security

What Identity Solution Providers Need to Know About Liveness Detection?

As a vital part of the IDV industry, identity solution providers or third-party identity vendors must be aware of technological advancements, regulatory updates, and benchmark practices to stand as top identity vendors. These biometric identity proofing solutions must consider the following factors pertinent to facial recognition and liveness detection.

1. Speed of Liveness Detection

Speed affects the success rate of liveness detection in facial recognition. Suppose an identity solution is capable of detecting face liveness in a manner of seconds and prompting the user with minimum gestures for active liveness or using Passive Liveness. In that case, it will speed up the anomaly detection. In this case, there’s a very minimal chance of a presentation attack or a deepfake attack going undetected.

It is important to note the active liveness test normally affects the overall speed of the facial identity verification process. The speed of liveness detection varies and depends upon the algorithm used in a facial recognition tool. 

Currently, Facia is the global leader in liveness verification through facial recognition by offering liveness verification in under one second.

2. Accuracy in Liveness Detection

There are four key metrics in verifying liveness under facial recognition.

This is the formula sheet and explanation of key metrics in facial recognition that includes: 1) False Match Rate (FMR), 2) False Non-Match Rate (FNMR), 3) False Acceptance Rate (FAR), 4) False Rejection Rate (FRR)
Liveness Detection MetricsDiscussion
False Match Rate (FMR)
  • It is the percentage of systems incorrectly matching an imposter (fake) identity with a genuine user’s face. 
  • It results in potential security breaches by falsely recognizing a fraudulent face.
False Non-Match Rate (FNMR)
  • It is the percentage of systems incorrectly failing or mismatching an imposter with an authentic facial identity.
  • It results in potential denials of legitimate users causing inconvenience and then customer loss.
False Acceptance Rate (FAR)
  • It is the probability that the system incorrectly authenticates a fake identity.
  • The higher the FAR, the higher the chance that presentation attacks and deepfakes get verified causing serious threats to the genuine user and the system.
False Rejection Rate (FRR)
  • It is the probability of the system incorrectly rejecting a genuine user and denying access.
  • A high FRR results in hurting user experience and causing frustration due to multiple sign-in attempts.


These 4 terms seem intertwined but they have slight differences:


  • FAR is the system’s tendency to accept unauthorized and potentially risky individuals by assessing them as legitimate users.
  • FMR is the incorrect matching of facial identities due to technical or human error that results in FAR.


  • FRR is the system’s flaw in rejecting a legitimate and authorized user considering it a potential threat.
  • FNMR is the incorrect mismatching of facial identities due to technical or human error that results in FRR.

A very important part is that both FMR and FNMR can cause FAR and FRR in different situations.

3. Cost / Benefit Analysis

The cost of a Facial Liveness Detection solution is critical in scaling a solution’s effectiveness as per the cost-to-benefit ratio. Let’s analyze the costs and benefits that both end-users and businesses can incur and derive from facial liveness detection.

  • Value for Customers

With the help of facial liveness detection, customers’ identities are safer than ever from spoof attacks and scams. Moreover, the personal accounts and information of the user will also be secured. It will not only establish a robust data protection mechanism but will also ensure an efficient user experience. Customers would not have to deal with the hassle of long verification steps but in reality experience a smooth login process vis-a-vis facial liveness detection.   

  • Value for Businesses 

Liveness detection is equivalent to having a security guard for your business operations. It will make sure that only real people enter so that fraud can be diminished and money saved. Moreover, liveness detection will ensure your good reputation if you are complying with all the regulations. This technology tends to scale as your business grows. So it will keep you ahead of the curve and develop a sustainable customer relationship as well.

  • Demerits For Customers

Facial liveness detection is crucial for security but concurrently it offers some hurdles along the way as well. The fear of personal face data being stored in the databases might be a cause of concern for people. On the other hand, fear of potential misuse might create a trust deficit with this technology leading to delayed onboarding and if outdated computer systems do not integrate well with this technology it will make it less accessible for everyone.  

  • Costs that Businesses Incur

Adding facial liveness detection to your business operation often leads to higher initial costs since it needs to be integrated with the prevalent systems. But just like any other security system, one needs to keep it updated and also train the relevant staff on it as well. This mainly includes things like software updates or complying with key regulations so one may need to pay some legal fees too. However, the good news is that all of this could be easily explained to your consumers via awareness campaigns. If they are informed of the value facial liveness detection offers, it will not only establish trust with the system but will make their whole investment worthwhile. 

4. Impact on business customer relationship

After understanding the cost-benefit analysis, it is clear that addressing the issue of customer data privacy and data protection is critical for businesses to protect their digital footprints and facial data present online. For this purpose, identity solutions like Facia are committed to protecting the data and only use it for verification purposes. Here, the data retention policy is of utmost importance because the end-user and businesses should know the policy of facial identity solutions for retaining and deleting customers’ facial data.

5. User Consent

The consent of end-users is important and they should be educated about the benefits of facial identity. If consent is not given, they should be provided with alternate identification means. However, facial identification is the future and will become the only passport users will have soon due to its unmatched benefits including the level of fool-proof security.

6. Transparency

Despite this, businesses must exercise a transparency policy before onboarding users through facial recognition and sharing with them to strengthen the business-customer relationship. They should inform them promptly about how the facial data will be used and they must be given an option to opt out of the system with a guarantee of deleting their facial identity data right away.

Also Read: Age Verification vs. Age Gating | Which One is Better for Age Assurance and Protection 

Frequently Asked Questions

What is liveness detection?

Liveness detection is a method to check if the face presented to the facial recognition system is real and not just fake. The purpose of this activity is to prevent fraud from taking place during facial recognition.

How does Liveness detection work?

Liveness detection works by scanning and seeing if the subject's face is real or not. For instance, it checks for movement like blinking or it might ask for specific actions like moving the face, hands, or speaking to ensure the liveness of the subject. This is called active liveness whereas passive liveness is much more advanced and checks user liveness through AI algorithms just by scanning the user’s face.