Dynamic Module Selection for Facial Recognition Accuracy
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Solution Overview
Problem
Existing image recognition techniques face challenges in accurately identifying individual objects under varying poses, expressions, and illumination conditions, especially as the number of individuals and states increases.
Innovation Solution
An image processing apparatus and method that detects the state of an object in an image and selects a corresponding individual identification process module to execute a specialized identification process, utilizing support vector machines and convolutional neural networks for robust identification across different facial directions and expressions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single individual identification process is used for all objects, then the device complexity is low, but the identification accuracy decreases when objects have various states (poses, expressions, illumination conditions)
Solution Approach 1:
The patent segments the individual identification process into multiple specialized process modules, each designed to handle specific object states (e.g., frontal pose, profile pose, smiling expression). The selection unit chooses the appropriate module based on the detected state of the object, enabling accurate identification for each state without requiring a single complex module to handle all cases.
2Measurement precision
If multiple individual identification process modules are used for different states, then the identification accuracy improves, but the device complexity increases
Solution Approach 1:
The patent implements a dynamic selection mechanism where the selection unit determines which individual identification process module to use based on the real-time detected state of the object. This dynamic adaptation allows the system to maintain high identification accuracy across various states while managing complexity through intelligent resource allocation rather than permanently activating all modules simultaneously.
3Adaptability or versatility
If the number of individuals and states to be identified increases, then the identification coverage improves, but the processing efficiency decreases
Solution Approach 1:
By dividing the identification task into specialized modules for different states, the system can process objects more efficiently by routing them to the most appropriate module rather than using a single general-purpose module that must handle all cases. This segmentation enables parallel optimization of each module for its specific state, improving overall processing efficiency while maintaining comprehensive coverage.
Solution Approach 2:
The patent applies partial action by activating only the specific individual identification process module needed for the current object state, rather than executing all possible modules. The selection unit determines the minimal necessary processing required based on the detected state, reducing unnecessary computational overhead while maintaining comprehensive identification coverage.
Data Source
AI summary
An image capturing unit acquires an image including an object. A state detection unit detects the state of the object in the image. An individual recognition processing unit determines one of a plurality of individual identification process modules in correspondence with the state detected by the state detection unit. The individual recognition processing unit executes, for the object in the image, an individual identification process by the determined individual identification process module.