Machine Learning Facial Recognition Using Predictive Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing facial recognition systems face challenges in accuracy due to varying poses and lighting conditions, often resulting in false identifications, and struggle to efficiently sift through large databases of facial identification records.
Innovation Solution
The implementation of machine learning enhanced facial recognition techniques that utilize a people model, period model, and event model to predict the likelihood of individuals being present at specific locations and times, adjusting confidence measures based on behavioral data and image quality, and grouping candidates to improve matching efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional facial recognition systems are used, then the system structure is simple, but the accuracy deteriorates due to false identifications from varying poses and lighting conditions
Solution Approach 1:
The system performs preliminary actions by predicting which individuals are likely to be present at a location before actual facial recognition occurs. Behavioral data and event models are used to pre-identify candidate individuals, so that when facial recognition is performed, the system only needs to verify pre-predicted candidates rather than searching through entire databases, thereby improving accuracy without proportionally increasing complexity
Solution Approach 2:
The patent introduces an intermediary layer between traditional facial recognition and the final identification result. This intermediary consists of prediction models that use behavioral data, event information, and temporal patterns to filter and prioritize potential matches. The intermediary adjusts confidence measures by combining traditional facial recognition scores with prediction-based weights, effectively mediating between raw recognition data and final identification decisions
2Productivity
If the system sifts through large databases of facial identification records, then the completeness of search is improved, but the processing time increases
Solution Approach 1:
The patent segments the large database of facial identification records into smaller, more manageable groups based on predictions about which individuals are likely to be present. Instead of searching the entire database uniformly, the system divides candidates into segments based on predicted presence probability, event attendance patterns, and temporal information, thereby reducing processing time while maintaining search completeness for relevant individuals
Solution Approach 2:
The system performs preliminary filtering using prediction models before executing full facial recognition searches. By pre-identifying which individuals are most likely to match based on behavioral data and event models, the system can prioritize those candidates for detailed verification, significantly reducing the time required to sift through large databases while maintaining high identification accuracy
3Reliability
If confidence thresholds are adjusted based on prediction, then the reliability of identification is improved, but the complexity of confidence calibration increases
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting confidence thresholds based on predicted presence probability and other variables. Instead of using a fixed confidence threshold, the system modifies the threshold parameter according to contextual factors such as event type, time of day, and individual behavior patterns. This allows the system to maintain high reliability by adapting thresholds to situation-specific requirements without requiring complex manual calibration
Solution Approach 2:
The confidence calibration system operates autonomously by automatically learning from historical data and adjusting thresholds without human intervention. The system self-calibrates by analyzing patterns in past identifications and automatically updating confidence parameters based on performance metrics, thereby improving reliability while avoiding the complexity of manual calibration processes
Data Source
AI summary
A technique of performing machine learning enhanced facial recognition. The technique includes accessing a facial image for a facial recognition target, performing facial recognition on the facial image, making a prediction regarding facial recognition candidates for the facial recognition target, and indicating a measure of confidence regarding the facial recognition performed on the facial image, with the measure adjusted based on the prediction. The prediction may be made based at least in part on a people model that statistically predicts the facial recognition candidates who may be present at a particular location at a particular time, a period model that predicts one or more times that the facial recognition candidates may be present at a particular location, behavioral data that indicates an intention of the facial recognition candidates to be at a particular location at a particular time, and/or actions such as purchasing tickets or registering for an event.