Image Recognition Using Multi-Dimensional Feature Analysis
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Solution Overview
Problem
Current image recognition methods fail to accurately distinguish between real and fake human faces in videos, leading to security and privacy concerns due to their high false detection rates and inability to cover fake faces generated by various face-changing algorithms.
Innovation Solution
An image recognition method that utilizes machine learning to analyze blur degree, local feature information, and global feature information to determine the authenticity of human faces, employing a multi-dimensional approach to improve efficiency and accuracy, and includes a system architecture with a terminal device and server for real-time identification and risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image recognition methods are used, then the recognition process is simple, but the accuracy of distinguishing real and fake human faces is low
Solution Approach 1:
The patent segments the image recognition process into multiple independent modules: blur degree detection module, local feature extraction module, global feature extraction module, and classification module. Each module processes specific aspects of the image independently, allowing the system to achieve high accuracy through comprehensive multi-dimensional analysis while maintaining manageable system complexity through modular design.
Solution Approach 2:
The patent transitions from traditional single-dimension recognition to multi-dimensional analysis by incorporating blur degree information, local features, and global features as separate analytical dimensions. This multi-dimensional approach enables the system to distinguish real and fake faces more accurately by examining the image from multiple perspectives simultaneously.
2Measurement precision
If multi-dimensional feature analysis is performed, then the accuracy of detection improves, but the processing time increases
Solution Approach 1:
The patent performs preliminary blur degree detection and local feature extraction before global feature analysis. By conducting preliminary assessments on simpler features first, the system can quickly eliminate obviously fake or real images, reducing the need for full multi-dimensional analysis on all images and thereby decreasing overall processing time while maintaining high detection accuracy.
Solution Approach 2:
The patent implements a hierarchical analysis approach where not all feature dimensions are processed with equal depth for every image. Based on preliminary detection results, the system adjusts the level of analysis applied to each image, performing partial analysis on images that can be confidently classified and excessive (full) analysis only on ambiguous cases, optimizing the balance between accuracy and processing time.
Data Source
AI summary
This application provides an image recognition method and apparatus, an electronic device, and a computer-readable storage medium, and relates to the field of artificial intelligence technologies. The method includes obtaining feature information corresponding to a target object in an image to be recognized, the feature information comprising blur degree information, local feature information, and global feature information; determining a category of the target object based on the feature information, and determining a confidence level corresponding to the target object; and obtaining target information corresponding to the image to be recognized according to the category of the target object and the confidence level.