Object Recognition in Images Using GAN-Based Appearance Synthesis
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Solution Overview
Problem
Existing image recognition technologies struggle to accurately identify individuals due to changes in outward appearances such as pose, clothes, and hairstyle over time or in different surroundings.
Innovation Solution
An electronic device uses a learning model, such as a generative adversarial network (GAN), to generate a new image based on extracted object information, considering pose and style, and compares it with the original image to recognize the object accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image recognition methods are used to identify persons, then the system is simple and fast, but the identification accuracy decreases when outward appearance changes
Solution Approach 1:
The system performs preliminary actions by generating multiple synthesized images showing the target person in various poses, hairstyles, and clothing styles before the actual identification process. This preprocessing step creates a comprehensive reference database that accounts for appearance variations, enabling accurate identification even when the captured image shows different outward features.
Solution Approach 2:
The system creates synthetic copies of the target person's images through GAN-based generation, producing multiple variations that simulate different appearances. These copied images serve as reference patterns for comparison, allowing the system to identify the person regardless of changes in pose, hairstyle, or clothing in the actual captured image.
2Measurement precision
If multiple factors such as pose, clothes, and hairstyle are considered for accurate identification, then the identification accuracy improves, but the complexity of the recognition system increases
Solution Approach 1:
The system segments the identification task into distinct components: pose detection, hairstyle recognition, and clothing analysis. Each component is processed separately through specialized neural network modules, allowing the system to handle multiple appearance factors independently and then integrate the results for comprehensive identification.
Solution Approach 2:
The system introduces an intermediary GAN-based synthesis module that acts as a mediator between the captured image and the identification process. This intermediary generates synthetic reference images that bridge the gap between different appearances, enabling accurate comparison and identification without requiring the system to directly handle all appearance variations simultaneously.
3Quantity of substance
If the system captures images at different times and surroundings, then more data is available for identification, but the variation in outward appearance makes recognition more difficult
Solution Approach 1:
The system deliberately changes key appearance parameters such as pose angles, hairstyle styles, and clothing types when generating synthetic reference images. By systematically varying these parameters to create diverse reference patterns, the system prepares for and accommodates the natural variations that occur when capturing images at different times and in different surroundings.
Data Source
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AI summary
Provided is a method, performed by an electronic device, of recognizing an object included in an image, the method including: extracting first object information from a first object included in a first image, obtaining a learning model for generating an image including a second object from the first object information, generating a second image including the second object by inputting the first object information to the learning model, comparing the first image with the second image, and recognizing the first object as the second object in the first image, based on a result of the comparing.