Entity Recognition via Dynamic Imaging Condition Adjustment
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Solution Overview
Problem
Current image recognition systems face challenges in distinguishing between physical and non-physical entities, leading to potential fraud in e-commerce and security applications, as they often rely on manual verification processes that are costly and inefficient.
Innovation Solution
A system that captures images of a to-be-recognized entity under varying imaging conditions, extracts features such as RGB values and size, and applies machine-learning techniques to determine if the entity is physical, using adjustments in lighting, distance, and direction to differentiate between real and counterfeit objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual verification processes are used to verify entity authenticity, then fraud prevention capability is improved, but operational efficiency and cost-effectiveness deteriorate
Solution Approach 1:
The patent replaces manual verification processes with an automated machine-learning-based entity recognition system. The system captures images under varying imaging conditions, extracts features, and automatically determines entity authenticity, eliminating the need for manual inspection while maintaining fraud prevention capability.
Solution Approach 2:
The entity recognition system performs self-verification by automatically analyzing captured images, extracting features, and determining whether entities are physical or non-physical without requiring external manual intervention. The system serves itself by integrating image capture, processing, and decision-making into a single automated workflow.
2Measurement precision
If multiple imaging conditions are used to capture entity images, then entity recognition accuracy is improved, but system complexity and processing time increase
Solution Approach 1:
The patent employs dynamic imaging conditions where the system varies illumination intensity, color temperature, and capture angles during the imaging process. This dynamic approach allows the system to capture entities under multiple conditions without requiring complex static setups, as the variations are introduced through controlled changes in lighting and camera parameters.
Solution Approach 2:
The system changes imaging parameters such as illumination intensity, color temperature, and capture angle to capture entities under varying conditions. By systematically varying these parameters, the system obtains multiple feature representations of the same entity, improving recognition accuracy without requiring fundamentally different imaging devices.
3Extent of automation
If machine-learning techniques are applied for entity recognition, then automation level is improved, but computational resource requirements and processing complexity increase
Solution Approach 1:
The patent extracts only the most relevant features from captured images, such as color distribution, texture patterns, and shape characteristics, rather than processing entire images. This feature extraction approach reduces the computational burden of machine-learning models while maintaining high automation levels for entity recognition.
Data Source
AI summary
Embodiments described herein provide a system for facilitating entity recognition. During operation, a camera associated with a computing device captures at least a first image of a to-be-recognized entity under a first imaging condition. The system adjusts the first imaging condition to achieve a second imaging condition, and the camera captures at least a second image under the second imaging condition. The system determines whether the to-be-recognized entity is a physical entity based on the captured first and second images and the first and second imaging conditions.