Top 10 Use Cases of Liveness Detection in Today’s Digital Age
Author: Carter | 14 Dec 2023In This Post
The digital landscape is constantly evolving, and with it, the need for robust security measures. A 2023 study by Boisestate found that cybercrime caused businesses an estimated $6 trillion in global losses in 2021. Consumers and businesses alike demand safe and efficient ways to interact online. Liveness detection has emerged as a powerful tool in this fight, offering a reliable solution for user verification.
This technology goes beyond traditional passwords and PINs, which are vulnerable to hacking and social engineering. By confirming a user’s physical presence through facial recognition or other biometric techniques, liveness detection adds an extra layer of security, preventing unauthorized access and fraudulent activities. This not only protects businesses from financial losses but also fosters trust among users. According to a McKinsey report, 87% of consumers surveyed in 2022 said they would be more likely to do business with a company that uses strong authentication methods.
Key Takeaways
- Presentation attack detection is done through iris, face and voice recognition measures.
- In 2022, the face recognition market reached $5 billion and is anticipated to grow by $19.3 billion by 2032.
- Liveness detection ensures that the authentic person captures the image or video.
In this blog post, we’ll explore top use cases of liveness detection across various industries. We’ll delve into how this technology is shaping a more secure and streamlined digital future.
How Liveness Algorithms Work?
Facial liveness detection algorithms investigate user videos and images to understand if they come from any imposter or a live person. This system algorithm uses 3D depth sensing, challenge-response tests, machine learning algorithms, and texture or motion analysis methods to cope with the different presentation attacks.
See What is Liveness Detection and How Does it Works: Live Demo
Conventional Presentation Attacks of Imposters
To bypass and manipulate facial verification algorithms that only verify user features, imposters deploy an array of impersonation gadgets. Also, these impersonation resources become trickier as cloud-based systems become accustomed to them. Therefore, the liveness detection industry refers to these tricks as presentation attacks or usually spoofing. In 2019, only 333 phishing attacks were recorded, which are increasing by the year, and by October 2022, imposters had targeted 599 multinational brands.
Let’s go through a few presentation attacks that are as follows:
Deepfake or Synthetic Video
Imposters create a deepfake or realistic version of a person nodding their head and talking whilst taking video or photo, by editing with an advanced animation software.
Video or Photo Attack
Typically, imposters gain access to videos or photos via a Google search or a user’s social media account.
3D Mask or Model
Imposters invest in multi-dimensional masks and customised models that mimic a client’s physical likeness.
What is Facial Liveness Detection that Limits Presentation Attack?
Liveness detection steps in to reduce presentation attacks as it detects whether the client is a live person. However, this system confirms the existence of a client’s verification credentials and if they are physically present on a mobile phone, computer, or any camera-enabled device. Two essential methods of incorporating liveness detection are:
Active
Active liveness detection is the typical option deployed in modern biometric technologies. By using it, users have to turn their heads, smile, or blink, and the system waits for the task completion. Usually, the software randomises the essential checks to secure against a synthesised user video that showcases the required actions.
Passive
Passive liveness detection needs no extra effort from the user. Rather than needing a distinct performance at every step, such as smiling, blinking, and turning the head, passive detection is invisible to imposters. Moreover, it provides no other way to inspect clients’ actions to recreate video replays to prevent attacks.
Top Use Cases of Liveness Detection
Whilst unlocking smartphones, many clients interact with this technology. This remarkable innovation is being applied in the law enforcement and security industry as it helps them easily find criminals. Now, it’s expanding to other industries and finding new ways. The international biometric identification and authentication market will reach $100 billion at a CAGR of 14.6% from 2019 to 2027.
From healthcare to entertainment and banking, different sectors harness liveness detection capabilities to simplify processes, provide personalised experiences, and strengthen security.
Let’s move further to understand how liveness detection serves different industries thoroughly.
Safety and Administration on College Premises
Liveness detection systems can provide hostels and colleges with different benefits. It improves access management, enables a seamless attendance tracking system, reduces administrative tasks, and strengthens security. With its remarkable accuracy and efficiency, this advanced technology is valuable for streamlining administrative processes and providing a safe environment.
Liveness Detection for Authenticating Banking Services
Facial liveness detection strengthens safety by offering an extra layer of verification for digital banking, ATM transactions, and mobile payment services. This AI-powered technology allows clients to securely and conveniently authorise transactions and access their accounts. Moreover, the customers must be aware of the risks of storing and accumulating facial data.
Improves Commercial Real Estate Safety
3D liveness detection has increasingly been utilised in commercial real estate to strengthen safety. This system helps to recognise tenants, tracks visitor movements, and restricts unauthorised access. Also, this improves the building security, contributing to a consistent tenant experience.
Ensures Patient Security in the Healthcare Industry
In the healthcare industry, liveness detection prevails as it swiftly recognizes and compares medical records with patients, enhancing administrative efficiency and patient security. Moreover, by evaluating emotions and facial clues to gauge emotional states and individual well-being, this system helps with telemedicine and remote patient investigations.
Use Cutting Edge Technology as a Password Alternative
It’s forecasted that liveness detection will replace all types of passwords as digital technology advances and the application portfolio broadens. That’s why companies require a streamlined technology that can control information access or a way that doesn’t need users to remember their multiple account credentials.
Airline Industry Uses Liveness Detection As an Alternative for Tickets
Airports that follow the liveness detection biometric process have traveller groups that stand in queues to bypass the scanning process. At the time of passenger onboarding, they move to the area where this advanced system is positioned. There may be marks on the ground that show the standing position. When they stand at the desired position, the scanner automatically notices their movements, analyses them, and compares them with the previous image ID kept in the database. However, if the system doesn’t recognize them, a boarding agent will manually review their boarding pass and ID.
Rewards Public Transit Clients
By utilising current infrastructure, liveness detection in public transit software can swiftly recognize and compensate commuters who select feasible transportation options. Commuters can earn fare discounts, align with sustainable developmental targets, and incentivize green commuters who choose trains, buses, and bicycles.
Get Real-Time Client Feedback
Firms are utilising liveness detection technology to monitor client emotions and reactions closely in real-time. This permits targeted product recommendations, and advertising depends on client preferences and facial expressions. However, this raises ethical concerns about client consent and privacy.
Liveness Checks Provide Accessibility to Keyless Cars
Facial liveness detection revolutionises owner accessibility to automobiles. The Genesis EV model leads the industry by removing the requirement for conventional key systems. Also, other car firms are expected to adopt this technology soon. Hence, stealing vehicles will be made difficult, making the car industry more convenient and secure.
Assists in Market Research
Soon, liveness detection will empower marketing research as the market research industry bypassed $81 billion in 2022, doubling since 2008. Enterprises can use this advanced technology in focus groups by making commercials or product demos, utilising a webcam to perform sentiment analysis, and registering participants’ reactions. Hence, it assists firms in real-time detection of the accurate moments in their marketing materials that remove emotional client responses, either expected or planned.
Improve Your Business Operations with Facia
Facia swiftly identifies imposters searching to exploit firms as their liveness detection method automatically detects live faces from non-live ones. Organisations have to identify that biometric technologies have additional security layers. Imposters always look for ways to bypass KYC, such as user onboarding and other legal procedures. Hence, anti-spoofing solutions or liveness detection is mandatory to secure biometrics.
Want to avoid spoof attacks and stay one step ahead of criminals?
Frequently Asked Questions
Liveness detection determines if biometric data, like a face or fingerprint, originates from a living person or a fraudulent source. It ensures security by distinguishing between real human features and fake representations, preventing unauthorized access.
Liveness detection refers to the ability of a biometric system to distinguish between a live genuine sample and a fake or spoofed sample. It ensures that the biometric data being presented during a verification or identification process is from a living person rather than from an artificial source.
Impersonation attacks involve an attacker pretending to be someone else, often by mimicking their identity, behaviour, or credentials. These attacks aim to deceive individuals, systems, or organizations to gain unauthorized access, extract sensitive information, or carry out fraudulent activities, exploiting trust in the impersonated identity.
A liveness detection replay attack involves using pre-recorded data, like videos or audio, to deceive a liveness verification system into thinking it's dealing with a live individual. This attack aims to bypass security measures, highlighting the importance of robust liveness detection to prevent impersonation and ensure the security of biometric systems.
Deep learning algorithms, notably Convolutional Neural Networks (CNNs), excel in face detection. They offer superior accuracy in recognizing faces within images and videos, making them the preferred choice for this task.
