Enhanced authenticity assists companies in building credibility, combating deception and fraud
In the ongoing battle against identity fraud, a new technology is making waves - passive liveness detection. This innovative method is revolutionising the way we verify user identities by focusing on what makes us truly human.
Passive liveness detection is a technique that verifies whether a biometric sample, such as a facial image or fingerprint, belongs to a living person. Unlike active liveness detection, which requires user interaction, passive detection operates transparently in the background. It examines subtle biometric cues like motion, depth, skin texture, reflection, and environmental factors to discern between a live person and a fake, spoofed, or AI-generated source.
The process begins with the system collecting biometric samples. This could be a facial selfie or a fingerprint scan. Machine learning models then analyse the image or video frames for signs of life, such as natural micro-movements, realistic skin texture, lighting consistency, and three-dimensional depth cues that are hard to replicate in photos, videos, masks, or deepfakes.
The system also looks for anomalies or irregularities typical of spoofing attempts, such as texture inconsistencies, unnatural reflections, focus anomalies, or motion patterns not consistent with a live human. Some systems even examine device environment and hardware data to detect potential manipulations or injection attacks.
Advanced passive systems can reach high accuracy, exceeding 95%, particularly when using deep neural networks and ensemble algorithms combining multiple biometric cues. This allows them to reliably distinguish genuine biometric inputs from spoofing and deepfake attacks.
The speed, accuracy, and efficacy of using machine learning to determine document liveness is far better than even the most expertly trained document analyst. This technology solves the problem of document spoofing by using deep neural networks to analyse images and determine the authenticity of documents.
By employing strong authentication methods without sacrificing the user experience, banks and other organisations can continue to earn the trust of their customers and differentiate themselves from competitors. Solutions that use biometrics and other user interactions to prevent identity fraud ensure strong KYC policies to demonstrate account security.
One such solution is MiPass, a biometric and liveness-based authentication solution that layers authentication processes on top of biometric enrollment in a leading-edge platform.
In a recent survey, nearly eight in 10 Brits expressed a desire for banks to adopt the latest technology to keep their accounts safe, with security being a priority over ease of access or account opening. Passive liveness detection is a step in this direction, providing a secure and seamless user experience.
In conclusion, passive liveness detection mechanisms employ AI and machine learning to scrutinise biometric images or video for authentic human characteristics automatically and invisibly, leveraging biometric texture, motion, depth, and environmental signals to prevent identity fraud without requiring active user engagement. This technology is set to play a crucial role in the future of biometric identity verification.
[1] Article on passive liveness detection [2] Article on deep neural networks in liveness detection [3] Article on the role of passive liveness detection in biometric security [4] Article on the accuracy of passive liveness detection
- To enhance cybersecurity in the finance sector, businesses are integrating facial recognition technology with passive liveness detection, ensuring that financial transactions are carried out securely and reliably.
- The future of technology-driven business operations lies in the incorporation of advanced biometric security measures, such as passive liveness detection, to bolster cybersecurity and facilitate seamless user experiences.
