In this world of digitization, facial liveness detection has revolutionized the systems by authenticating the customer’s identity digitally. It has made digital interactions easy and secure by employing advanced techniques of artificial intelligence (AI) and machine learning (ML). These technological advancements work on the basis of algorithms that authenticate user identity in a minimal amount of time. By introducing security frameworks, liveness detection has improved the reliability of online applications.
In this blog post, there will be a detailed study of the facial liveness detection technique and how it works in the digital framework.
Evolution of the Global Facial Recognition Market: 2020 to 2025
In 2020, the worldwide market for facial recognition technology was valued at $3.8 billion. Experts predict that by 2025, it will be worth double that amount, reaching around $8.5 billion. This means the demand and use of facial recognition systems are expected to grow significantly over the next few years, showing how important this technology has become in various industries.
Active and Passive Liveness Detection
Active liveness detection asks users to perform some specific facial movements. These particular actions are smiling, blinking and movements of eyes and face. This is specifically done to prove the liveness of the user activelly.
Passive liveness does not require any type of action from the user. Meanwhile, it detects the background activity of the user to identify any type of spoofing activity. The system uses machine learning and artificial intelligence (AI) algorithms to identify spoofing attacks. Passive liveness checks work to identify unusual movements without asking the user.
Working Mechanism of Liveness Detection to Identify Spoofing Attacks
Facial liveness detection works in a series of steps that ensure the validity of the live user’s presence utilizing advanced technologies. A detailed explanation of the working phenomenon of the face detection process is described below:
- Prompting actions
- Facial data capture
- Scrutinizing through algorithms
- Extraction of Facial feature
- Comparison and authentication
- Anti-spoofing measures
- Decision-making and granting access
Prompting Actions
The facial liveness detection starts by requesting the user to perform some particular actions that will demonstrate the liveness. These specific actions are blinking, smiling, nodding, and turning the head from side to side. The system differentiates between the live person and a static image or a pre-recorded video after the user performs the actions.
Facial Data Capture
As soon as the user performs the required actions such as smiling and blinking, the real-time facial data is captured by the system’s camera. The captured data holds various types of facial features like muscle movement , variations in facial expressions, and overall face structure..
Scrutinizing through Algorithms
Facial detection techniques utilize artificial intelligence (AI) and machine learning (ML) algorithms to scrutinize the captured data. These advanced algorithms are designed in such a way that the system detects the pattern that differentiates between the live human and the static person.
Extraction of Facial Features
During the analysis phase, the system focuses on extracting facial features. These dynamic features include the frequency and speed of the blinking, the smiling style, the angle of the head movements, and other behavioral activities that reflect the user’s liveness. The system also considers other factors like skin texture, and reflection of the light on the face, which are difficult to replicate in static videos or images.
Comparison and Authentication
Face detection online compares the extracted facial features against a threshold and the data stored in the system. This comparison mainly aims to verify the observed face movements and features that align with the expected behavior of the live person.
Anti-Spoofing Measures
The facial liveness detection technique integrates anti-spoofing measures into the system to enhance the security measures and make it more reliable. The anti-spoofing measures detect unnatural patterns and deep analysis of the texture information using 3D facial detection recognition procedures. It also incorporates biometric face recognition modules for multi-factor authentication for iris scanning and voice recognition. It is known as the ultimate solution for face spoof detection.
Decision-Making and Granting Access
The system makes a final decision based on the results of the verification analysis and outcome. The access is granted to the users for the requested service after successfully passing the facial liveness detection test. But if the system detects any type of spoofing activity or non-liveness, the access gets declined and security protocols are activated for security purposes.
Final verdict
The facial liveness detection procedure provides the ultimate solution for identifying spoofing attacks in digital verification. As the world is moving towards digital systems, users are getting more familiar with the working mechanism therefore, they design hacking strategies to make the system faulty. To avoid all the monetary losses and identity threat scams, it is mandatory for digital applications to adopt 3D facial liveness procedures for combating virtual scams.
