Machine learning based system and method for optimized real-time identity verification of users on edge devices
OwnerSanjay Krishnamurthyinventor
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Abstract
A machine learning (ML)-based method and system for optimized real-time identity verification of users on edge devices, is disclosed. The process begins by obtaining first inputs associated with users through image capturing units of the edge devices, using SDK configuring on the edge devices. The process is followed by generating first embeddings from the first inputs using feature extraction models executing on the edge devices. The process includes performing at least one of: liveness detection and deepfake detection on the edge devices based on the generated first embeddings, using a ML-based classifier executing on the edge devices. The first embeddings are stored in secured local storage of edge devices.
Claims20
A machine learning (ML)-based method for optimized real-time identity verification of one or more users on one or more edge devices, the ML-based method comprising:
- obtaining, by one or more hardware processors, one or more first inputs associated with the one or more users through one or more image capturing units of the one or more edge devices, using a software development kit configuring on the one or more edge devices;
- generating, by the one or more hardware processors, one or more first embeddings from the one or more first inputs associated with the one or more users, using one or more feature extraction models executing on the one or more edge devices, wherein raw biometric data from which the one or more first embeddings are generated is processed entirely on the one or more edge devices and is not transmitted externally from the one or more edge devices;
- performing, by the one or more hardware processors, at least one of:
- liveness detection and deepfake detection, on the one or more edge devices based on the generated one or more first embeddings, using a ML-based classifier executing on the one or more edge devices;
- and
- storing, by the one or more hardware processors, the generated one or more first embeddings in a secured local storage of the one or more edge devices.
A machine learning (ML)-based system for optimized real-time identity verification of one or more users on one or more edge devices, the ML-based system comprising:
- one or more hardware processors;
- a memory coupled to the one or more hardware processors, wherein the memory comprises a plurality of subsystems in form of programmable instructions executable by the one or more hardware processors, and wherein the plurality of subsystems comprises:
- an input obtaining subsystem configured to obtain one or more first inputs associated with the one or more users from one or more image capturing units of the one or more edge devices, using a software development kit configuring on the one or more edge devices;
- an embeddings generating subsystem configured to generate one or more first embeddings from the one or more first inputs associated with the one or more users, using one or more feature extraction models executing on the one or more edge devices, wherein raw biometric data from which the one or more first embeddings are generated is processed entirely on the one or more edge devices and is not transmitted externally from the one or more edge devices;
- a liveness determining subsystem configured to perform at least one of:
- liveness detection and deepfake detection, on the one or more edge devices based on the generated one or more first embeddings, using a ML-based classifier executing on the one or more edge devices;
- and
- an embeddings storing subsystem configured to store the generated one or more first embeddings in a secured local storage of the one or more edge devices.
A non-transitory computer-readable storage medium having instructions stored therein that when executed by one or more hardware processors, cause the one or more hardware processors to execute operations of:
- obtaining one or more first inputs associated with one or more users from one or more image capturing units of one or more edge devices, using a software development kit configuring on the one or more edge devices;
- generating one or more first embeddings from the one or more first inputs associated with the one or more users, using one or more feature extraction models executing on the one or more edge devices, wherein raw biometric data from which the one or more first embeddings are generated is processed entirely on the one or more edge devices and is not transmitted externally from the one or more edge devices;
- performing at least one of:
- liveness detection and deepfake detection on the one or more edge devices based on the generated one or more first embeddings, using a ML-based classifier executing on the one or more edge devices;
- and
- storing the generated one or more first embeddings in a secured local storage of the one or more edge devices.
Dependencies are inferred from the claim text.