Edge XGBoost Image Recognition for Low-Latency Authentication
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Solution Overview
Problem
Existing automated image recognition systems face high latency, low accuracy, and security concerns, particularly in point-of-service transactions, due to the need for extensive training data and reliance on graphics processing units (GPUs), and require personal information like phone numbers or email addresses for verification.
Innovation Solution
A system utilizing an XGBoost image recognition machine learning model with edge computing and blockchain authentication, which processes transfers securely and efficiently by capturing images, identifying features, and matching them with reference databases without requiring personal information, and using proof-of-stake blockchain for approval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If convolutional neural networks (CNN) are used for image recognition, then image recognition capability is provided, but latency increases and processing speed decreases
Solution Approach 1:
The patent extracts and removes the GPU dependency and complex CNN architecture from the image recognition system. By using alternative algorithms that do not require GPU hardware and extensive training data, the system eliminates the latency bottleneck while maintaining recognition capability.
Solution Approach 2:
The patent replaces expensive, computationally intensive CNN models with lighter, faster alternative algorithms that require minimal training data and can be executed efficiently on standard hardware, effectively using simpler, faster-processing methods instead of complex persistent models.
2Ease of operation
If automated processing is implemented at point of identification, then user convenience is improved, but security risks increase due to personal information requirements
Solution Approach 1:
The patent extracts and removes the requirement for personal information (phone numbers, email addresses) from the automated processing system. By using alternative verification methods that do not depend on personal data, the system maintains automation convenience while eliminating the security risks associated with personal information collection and storage.
3Measurement precision
If CNN with extensive training data is used, then recognition accuracy improves, but training time and computational resources increase significantly
Solution Approach 1:
The patent replaces expensive, time-consuming CNN training processes with alternative algorithms that require minimal or no training data. The system uses methods that can achieve effective recognition accuracy without the extensive computational investment and time required for traditional CNN training.
Solution Approach 2:
The patent changes the fundamental parameters of the image recognition approach by switching from CNN architectures that require large training datasets to alternative algorithms that achieve comparable accuracy with minimal training requirements, fundamentally altering the resource-time tradeoff.
Data Source
AI summary
An apparatus may include conduction of a secure transfer at a point of identification using image recognition and blockchain authentication. The apparatus may include a computer processor, a camera, an image capturing machine, a reference database, an XGBoost image recognition machine learning model, and a blockchain. The computer processor may be an edge computing device. The computer processor may be configured to identify a user at a point of identification by capturing an image, running the Features detection engine to identify sets of features, running the XGBoost image recognition machine learning model to identify labels in the sets of features, and matching the sets of features and labels to a record in the reference database. The computer processor may further be configured to seek user approval on the user's portable device before providing the transfer. Once the user has approved the transfer, the transfer may be recorded in the blockchain.