Facial Recognition Engine Selection by Image Parameters
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Facial recognition systems face challenges in efficiently identifying individuals in varying facial image conditions due to differences in image parameters such as size, brightness, contrast, and obstructions, leading to inconsistent performance across different facial recognition engines.
Innovation Solution
A facial recognition system that selects a subset of facial recognition engines based on ascertained image parameters, such as size, brightness, and obstructions, to compare new facial images with facial models in a database, and reports identified individuals to building automation systems for control actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single facial recognition engine is used, then the system is simple to operate, but the identification accuracy varies under different image conditions
Solution Approach 1:
The system dynamically selects facial recognition engines based on real-time image parameter analysis. Instead of using a fixed single engine or always using all engines, the system adapts the configuration by evaluating image characteristics (brightness, contrast, size, obstructions) and selecting the most appropriate engine subset for each specific image, thereby improving reliability while managing complexity through conditional logic
Solution Approach 2:
The system changes operational parameters by analyzing image parameters (brightness, contrast, size, obstructions) and using these parameters to determine which facial recognition engines to deploy. This parameter-based selection approach allows the system to optimize identification accuracy for different image conditions without requiring a complete system redesign
2Reliability
If multiple facial recognition engines are used for all images, then the identification accuracy improves, but the processing time increases
Solution Approach 1:
The system applies partial action by selecting only a subset of facial recognition engines based on image parameters rather than always deploying all available engines. This selective approach ensures sufficient processing power is applied to each image type without the overhead of running all engines on every image, thereby reducing processing time while maintaining identification accuracy
Solution Approach 2:
The system dynamically adjusts the number and type of facial recognition engines deployed based on real-time image analysis. By evaluating image characteristics and selectively activating only the necessary engines, the system optimizes the balance between processing speed and identification accuracy for each specific image
3Reliability
If facial recognition is performed on all images, then the identification coverage is complete, but the energy consumption increases
Solution Approach 1:
The system performs facial recognition processing selectively based on image parameter evaluation. By analyzing image characteristics first and then determining whether facial recognition should be performed and which engines to use, the system avoids unnecessary processing on images that don't meet certain criteria, thereby reducing energy consumption while maintaining complete identification coverage for relevant images
4Measurement precision
If image preprocessing is applied to all images, then the identification accuracy improves, but the processing complexity increases
Solution Approach 1:
The system performs preliminary analysis of image parameters (brightness, contrast, size, obstructions) before selecting facial recognition engines. This preliminary action allows the system to prepare appropriate processing approaches in advance based on image characteristics, improving measurement precision while managing processing complexity through structured pre-evaluation
Data Source
AI summary
A facial recognition system includes a memory for storing a facial image database, wherein the facial image database includes a plurality of entries each corresponding to a different person, and wherein each entry includes a person identifier along with one or more facial images of the person. The facial recognition system further includes a facial recognition module that is operatively coupled to the memory. The facial recognition module is configured to receive a new facial image, and to select one or more facial recognition engines based on one or more facial image parameters of the new facial image, and to use the selected facial recognition engines to compare the new facial image with facial models that are based upon facial images stored in the facial image database in order to identify the person in the new facial image.