Face spoof detection has transformed the world by enhancing security measures in particular areas of finance, healthcare, corporate, and education by authenticating identities. Spoofed images or videos and real faces are examined through advanced algorithms of artificial intelligence (AI). It also helps in checking unauthorized access and illicit pursuits.
As the world is getting advanced in digital systems, hackers are trying to overcome the networks by doing illegal activities. In 2022, people in the United States reported about 1,108,609 cases of identity theft to the Federal Trade Commission (FTC). This number is lower than the previous year when there were 1,434,676 reported cases of identity theft. So, in the increased illicit pursuit, liveness techniques for face spoof detection play an essential role.
How to Combat Spoofing through Face Liveness Detection Technique?
There are various procedures for countering face spoofing and all come under the liveness detection technique. Liveness detection ensures to identify if a face is live or it is created by a cybercriminal. In short, it helps in identifying the real face and the replica.
There are two major approaches in liveness detection: passive and active liveness detection. The active liveness detection technique identifies users by evidenting their “liveness” by speaking with a facial recognition system. On the other hand, passive liveness detection does not require any action from the user and automatically exam the background activity to identify any type of spoofing attack. The prominent difference between the two methods is user involvement.
Active Liveness Detection
Active liveness plays a vital role in face detection online. It is an advanced approach to detect scams. Users are required to come in front of the camera to carry out some actions to give a demonstration of their authenticity. The actions taken by the users are smiling, blinking, or sometimes nodding. In some extreme cases, the required actions would be randomly acquired by users to add an extra layer of security. Users are not allowed to access the system until they complete all required actions.
The option between passive and active detection checks relies on user experience and balance between security. Passive checks are required at user convenience, while active checks are required in cases of higher security.
Passive Liveness Detection
Passive liveness provides user convenience in the face detection process. It is considered user-friendly and less interrupting as it does not require any actions performed by the consumers. It detects liveness while reducing user interaction.
Users do not discover that they are being tested during this liveness detection. It works on its own and uses advanced techniques to detect spoofing activities in the background. Passive liveness checks use modern sensors and algorithms to examine biometric data such as pulse detection, thermal signatures, or facial movements without user involvement.
Robust Anti-Spoofing Technique to Identify Scams
There are various advanced liveness detection techniques to identify the spoofing activities based on texture, color, shape, reflectance, or movement to find more about how to reduce the scamming activities:
- Active Flash
Active flash minimizes the risk of presentation attacks by allowing the users to utilize the reflections of light on the face to verify their identity. It necessitates the utilization of changing lightning environments to be made feasible by more light produced by the device’s screen. White light helps identify facial features more accurately.
- Eye Blink Detection
Eye blink detection is considered the most accurate in face detection and recognition. The average person usually blinks 25-30 times per minute, with every one blink long lasting for 250 milliseconds. Videos with short breaks are captured between consecutive images to examine them with the human capacity of blinking. The number of blinks helps detect face spoofs.
- Deep Learning
This method for face spoofing detection uses a convolutional neutral network, commonly known as CNN, used to differentiate between real and fake pictures. CNN is an artificial intelligence-based procedure that governs pixel data.
- 3D Cameras
3D cameras are abundantly used in identifying the spoofing process. This advanced tool has the ability to differentiate between a flat shape and a real face. They offer high-accuracy defense against presentation attacks.
- Challenge-Response Techniques
The challenge and response technique authenticates the identity of the user by carrying out a series of challenges, such as the way of smiles, facial expressions of sadness & happiness, and movement of the head.
Final Interpretation
With the increasing demand for verifying procedures in this digital world, biometric face recognition and liveness detection methods are accurate ones. Liveness detection for face spoof detection plays an important role in spotting fakeness. Industries should divert their verification systems towards liveness approaches as they utilize artificial intelligence means to provide reliability. In this way, user authentication would be ensured, and the chances of illicit activities like money laundering, identity theft, and related crimes would be minimized.
