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Single selfie capture to reduce enrolment time and decrease abandonment rate

Single selfie capture to reduce enrolment time and decrease abandonment rate

Liveness detection is a crucial step in the verification process to determine if a person being verified is a real human being. With emerging threat of deep fakes impersonalisation, the standard liveness detection technology today such as active liveness is no longer adequate.

greenID’s breakthrough passive liveness detection technology not only does not require user to perform any additional, it is done in the same instance as the face match selfie is captured. Leveraging on deep neural networks and proprietary algorithms, our passive liveness check is capable of detecting liveness in the background based on a single selfie image. In addition to delivering high accuracy, our passive liveness approach is imperceptible to fraudsters and completely frictionless for users.

Single-image passive liveness detection with
proven high accuracy performance

  • Running in the background of the facial verification process, hence, more difficult to spoof.
  • It only needs one frame to tell if the person is real and alive, eliminating the requirement for additional user actions or gestures
  • Achieved through combination of unique deep neural network machine learning and calibrated real-world data analytics.
  • First passive liveness detection technology to be iBeta level 1 and 2 ISO 30107-3 compliant.
  • Achieved perfect score in detecting all spoofs and identifying all real users (bona fide) correctly in ISO/IEC 30107-3 Presentation Attack Detection (PAD) test.

  • Facebook Deepfake Detection Challenge gold medal winner