Gelatin fingerprints, false irises, and special eyewear can readily deceive biometrics systems and their liveness detection capabilities in hollywood movies. Those deceptions aren’t going to fool today’s technology for a second. Biometric technology has grown demonstrably more accurate and immune to attacks thanks to AI and, more specifically, deep learning where a system can learn from data. Deepfakes, bots, and advanced spoofing attacks have made certified liveness detection a must-have feature in any biometric-based verification solution.
What is Liveness Detection
In biometrics, liveness detection refers to a system’s capacity to determine whether a fingerprint, face, or other biometric is genuine (taken from a live individual present at the time of capture) or not (from a spoof artifact or lifeless body part). Biometric systems have been widely used around the world in response to the demand for accurate and secure authentication and authorization. Designing a biometric system that is resistant to existing and future spoof attacks is a major problem for the industry today. Fraudsters represent a wide range of hazards, all of which are extremely dangerous as they have access to cutting-edge technology such as resin 3D printers with a resolution of 25 microns and artificial intelligence. In essence, a liveness detection mechanism is a security feature designed to reduce the risk of spoofing attacks on biometric systems.
Facial Liveness Detection
Accuracy is no longer an issue when utilizing face biometrics for authentication. Spoofing attempts using printed photos, recordings, deep fake pictures, and 3D masks, on the other hand, pose a serious threat. Face recognition can reliably answer the question, “Is this the right person?” but not the question, “Is this a live person?” This is where liveness detection comes in. Face biometric matching must be able to detect spoofs in order to be trusted, as well as to maintain the integrity of our biometric data. In other words, due to liveness detection, our biometrics don’t have to be kept a secret, which is a good thing considering how many of us have a lot of photos and videos online.
Active Liveness detection
Users are required to respond to “challenges” such as head motions, smiling, and blinking, which takes time and effort. This method is more reliable and trustworthy than passive liveness, but we have seen a move from active solutions to today’s modern, passive liveness detection is being driven by the fact that firms are increasingly valuing user experience as a strategy to attract and keep customers. Active methods show better success.
Passive Liveness detection
Because the user is not required to take any action, there is less friction and user abandonment during procedures like remote customer onboarding. A passive liveness detection can be performed using a variety of methods, such as evaluating a selfie photograph, recording a video, or flashing lights on the person.
IDcentral’s Liveness detection solution
IDcentral integrates certified liveness detection to thwart the many well-documented vulnerabilities in less robust liveness technologies which are susceptible to spoofing.
Features of IDcentral’s Liveness detection:
- Face Mapping
- Ability to detect micro-movements
- Active / Passive modes to improve customer throughput
- SDK based Integration
- Highest accuracy rates in the industry (Overall Accuracy 90%+. FMR ~0.2%, FNMR <10%.)
- Handles spoofing attacks via mobile video, paper masks, eye hole masks, latex masks
Know more about IDcentral’s Liveness detection feature