A new framework for an eKYC system

Identity verification has long been a crucial problem to solve to automate financial operations which requires user authentication and detect fraudulency. Until recently the realization of this task was nearly impossible to do with considerable accuracy, thanks to advancements in machine learning over the past few years we can achieve this task. This paper will discuss a proposed solution for high accuracy, high-performance eKYC system. In an eKYC system, we need to verify our client's identity as per his identity documents with the constraint that he passed a liveness detection test to ensure he is doing the financial operation in person. In our proposed system, verification is done in three main stages, which are: face detection, face verification, and face antispoofing detection. We employed an AI model to perform each task, We employed MTCNN [1] for face detection and FaceNet [12] for face verification. For face antispoofing, we implemented a state-of-the-art model PatchNet [15].