How Face Liveness Detection is Used in Authentication and Authorization

Blog 31 Jan 2023

Face liveness detection is an authentication and authorization method that uses facial recognition technology to detect and verify the identity of a user. It is used to verify that the person providing the authentication credentials is indeed the person who should be accessing the system. Facial authentication is becoming increasingly common in the current technologically sophisticated age. 

Various enterprises use liveness detection systems for authentication and authorization worldwide. Many industries are developing identity verification systems to assist financial institutions. Face liveness detection systems based on artificial intelligence technology are advanced methods for verifying customer’s identities through facial features and textures. The system will first use these methods to confirm the person’s identity and then use liveness detection to ensure that the person is present and not using a pre-recorded video or 2D photograph.

Face liveness detection captures a person’s facial features and compares them to a database’s pre-stored set of facial features. If the components match, the user is authenticated and authorized to use the system. Face liveness detection is becoming increasingly popular for authentication and authorization processes, especially for online banking and other sensitive transactions. It is a secure, reliable, and cost-effective method for verifying a person’s identity and ensuring that only the right people can access a system.

What is face liveness Detection?

Face liveness detection is used to determine whether a face in a digital image or video is that of a real person or a spoof. This is often used as a security measure to prevent fraud, such as using a photograph or a video recording of a person to gain unauthorized access to a system.

Face liveness detection can be accomplished using a variety of approaches, including:

Passive methods

These methods rely on analyzing the properties of the face image or video, such as lighting and texture, to determine if it is likely to be a natural face or a spoof.

Active methods

These methods require the person being verified to perform specific actions, such as blinking, nodding, or smiling. This is because real human faces will have natural movements and expressions that can be used to distinguish them from spoofs.

Hybrid methods 

Combine passive and active techniques to provide a more robust and accurate liveness detection.

Face liveness detection aims to ensure that the person attempting to access a system is physically present rather than relying solely on a photograph or video recording.

What is Authentication?

Authentication is the process of confirming a user’s or a system’s identification based on specified qualities or attributes. In machine learning, authentication is typically used to validate a user’s or system’s identity based on their unique characteristics or features, such as their face, voice, fingerprint, or iris.

There are several different machine-learning techniques that can be used for authentication, including:

Biometric authentication: 

This involves using a person’s unique physical or behavioral characteristics, such as their face, voice, fingerprint, or iris, to verify their identity.

Knowledge-based authentication

It involves using information only the genuine user is supposed to know, like a password or a PIN, to verify their identity.

Token-based authentication 

It involves using a physical device, such as a smart card or a token, to verify the user’s identity.

Multi-factor authentication

Primarily involves using different authentication methods, such as a password and a fingerprint scan, to verify the user’s identity.

Machine learning models are trained with data sets of the characteristics or features of the users and then deployed to authenticate the users. The models can be prepared with supervised or unsupervised learning techniques, depending on the type of authentication system being developed. Overall, authentication in machine learning is an essential aspect of security, as it helps to ensure that only authorized users or systems can access sensitive information or perform a specific action.


How Face Detection is used to solve authentication Issues?

Authentication is the process of validating a user’s or system’s identification. There are various approaches for dealing with authentication issues, including the following:

  • User IDs and passwords: This is the most basic form of authentication, where a user must enter a unique username and password to access a system or application. The system compares the entered information to a database of valid user IDs and passwords; if they match, the user is granted access.
  • An additional layer of security is provided by two-factor authentication, which requires the user to provide two forms of identification. Passwords, fingerprints, or passwords and one-time codes sent to a registered phone number can be used as authentication methods.
  • The biometric analysis uses the user’s physical characteristics, such as a fingerprint, face, or iris scan, to verify their identity. Biometric data is collected and compared to a stored template to determine if the user is whom they claim to be.
  • PKI encrypts and decrypts messages using a combination of public and private keys, ensuring that only the intended recipient can read them. This can be used for secure online communication and digital signing.
  • Single Sign-On (SSO): SSO allows a user to use one set of login credentials to access multiple systems or applications. This eradicates the need for the user to remember multiple usernames and passwords and improves security by centralizing user management.

Each method has advantages and disadvantages, and the appropriate way depends on the system or application’s specific requirements.

How does face liveness detection work?

Face liveness detection method used to determine if a user presented during the face authentication process is that of a live person or a photograph or video. It works by analyzing the characteristics of a face in real-time to determine if it is a live person or a recorded image. There are several technical methods that can be used for face liveness detection, including:

  • Motion-based detection analyzes the movement of the person’s face, such as facial expressions and head movements, to determine if they are alive. It can detect if the person is blinking, nodding, or moving their head, which are characteristics that would be difficult to replicate in a photograph or video.
  • Texture-based detection analyzes the texture of the person’s skin to determine if it is life. It can detect subtle variations in the skin’s surface, such as the presence of sweat or changes in color due to blood flow, which are characteristics that would be difficult to replicate in a photograph or video.
  • 3D-based detection uses depth cameras, such as infrared cameras, to capture a 3D image of the person’s face. It can detect the presence of depth and facial features that are difficult to replicate in a photograph or video, such as the distance between the eyes or the shape of the nose.
  • Artificial Intelligence/Machine Learning-based detection: This method uses AI/ML algorithms to analyze a person’s facial features and movements to determine if they are alive. It can detect subtle variations in the person’s facial expressions and movements, such as the movement of the pupils or the shape of the mouth, which are characteristics that would be difficult to duplicate in a photograph or video.

Depending on the system’s specific requirements or application, each method has its own advantages and disadvantages. It’s common to use a combination of these methods for better accuracy and robustness.

Face Liveness Detection: A Key for Safe Biometric Authentication Systems

Face Liveness Detection is key for safe biometric authentication systems because it helps to prevent spoofing attacks. A spoofing attack is when an attacker attempts to access a plan by presenting a fabricated biometric sample, such as a photograph or a fingerprint replica. Liveness detection method helps to ensure that the biometric sample being presented is from a live person rather than a fabricated or pre-recorded sample, thus increasing the system’s security. This helps to prevent fraud and increase the security of the authentication or recognition process. Several methods are used for face liveness detection, including analyzing the face’s movement, the skin’s texture, and the reflection of light in the eyes. Some systems use these methods to provide a more robust and reliable form of liveness detection.

FACIA provides Face Authentication With 3D Liveness Detection

A robust face liveness verification system utilizing PAD, AI, and ML is necessary to detect the growing number of facial spoof attacks. Fraudsters can easily bypass the system without features such as liveness detection, 3-D depth perception, and AI mapping, resulting in significant financial losses for a company. Facia’s face Authentication offers quick 3d liveness detection as an effective tool for preventing biometric fraud. 

The system analyzes liveness markers and compares indicators such as eye color, texture, and 3D analysis. Facia also performs depth perception analysis, presentation attack detection, and micro-expression analysis to ensure the user is genuine before authenticating their identity. The system uses pre-digitized templates and specifically designed algorithms for accurate comparison. Facia’s 3d liveness detection method confirms a person’s presence and protects against facial spoofing attacks.