We authenticate users by applying multiple proof-of-liveness / attack detection techniques to a still image or video (“secure selfie”). Pattern matching AI compares the secure selfie to a government-issued photo ID and/or images mined from social media or proprietary sources to attach a verified identity. The secure selfie is then converted into a 3-D mask and a biometric EgHash™ which is stored on a server and/or identity Blockchain. The EgHash™ can be used for subsequent authentications or to attach and access relevant data.
The future of authentication is multi-factor with active authentication supplemented “passive” techniques. Unfortunately, each technique creates its own “identity” which is then tied back to the forms of PII that are causing the synthetic identity crisis, and each new “identity” needs to be integrated into enterprise data flows causing expensive implantation challenges.
Our patented EgHash™ uses AI facial biometrics to store an encrypted EgHash™ unique to the user’s face, however, the EgHash™ architecture can use (or add) any alternate authentication and every EgHash™ is comparable regardless of the method. The EgHash™ protects against system and data redundancy providing a lifelong “digital-DNA” that can store (or pivot to) any type of KYC or relationship data with fields individually hashed or (salted and) encrypted, facilitating selective data sharing.
Our solutions can be delivered on premises and integrated within an enterprise code base or as a containerized solution, via API, as a blockchain solution and / or built, hosted and 100% managed by us. We can achieve this partly because of our investment in MicroService architecture and partly because our own expert AI researchers and engineers are housed within a diverse 100+ engineer partner-enterprise that gives us the flexibility to rapidly scale up and down as needed. This also enables us to deliver fully effective proofs of concept with minimal integration requirements allowing ideas to be quickly tested and iterated.