Facial Expression Recognition Normalization via Universal Ranking
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Solution Overview
Problem
Existing facial expression recognition technologies fail to account for individual differences in expressiveness, leading to inconsistent and subjective evaluations of facial expressions, which hinders accurate emotion recognition and affective inference.
Innovation Solution
A processor-implemented method that extracts features from input images using neural networks trained with diverse facial expressions, estimates expression intensity, normalizes expressiveness using a universal ranking system, and recognizes facial expressions based on pre-defined normalization functions, enabling objective and standardized emotion analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If facial expression recognition is performed using traditional image processing methods, then the system is simple to implement, but the measurement precision of expressiveness is low due to individual differences
Solution Approach 1:
The patent transforms the expressiveness measurement from absolute intensity values to relative ranking parameters. By converting expressiveness values into ranked positions (e.g., 1st, 2nd, 3rd) among multiple images, the system achieves universal comparability across individuals while maintaining measurement precision. This parameter transformation resolves the contradiction by enabling precise cross-individual comparison without requiring complex individual calibration systems.
2Reliability
If individual-specific expressiveness thresholds are used for each user, then the emotion recognition accuracy is improved, but the device complexity and data requirements increase significantly
Solution Approach 1:
The patent creates a universal expressiveness ranking system that works across all users without requiring user-specific calibration. The ranking mechanism universally compares expressiveness levels among images for any given user, enabling the system to adapt to individual differences through the ranking process itself rather than through user-specific threshold databases. This universal approach maintains high reliability while avoiding the complexity of individualized systems.
Solution Approach 2:
The system performs self-calibration by automatically establishing expressiveness rankings based on the user's own image set without requiring external calibration data or manual setup. The ranking process inherently adapts to each user's facial characteristics and expressiveness patterns, enabling the system to serve itself rather than requiring complex external configuration for each user.
3Measurement precision
If multiple neural networks are used to extract features and estimate expressiveness, then the measurement precision is improved, but the loss of time for processing increases
Solution Approach 1:
The patent divides the processing into distinct functional segments: feature extraction using a first neural network, expressiveness estimation using a second neural network, and ranking using a third neural network. This segmentation allows each network to specialize in a specific task, improving overall precision while enabling parallel processing optimization. The modular architecture reduces processing time compared to a single monolithic network by allowing independent optimization and potential parallel execution of segments.
Data Source
AI summary
A processor-implemented method includes obtaining an input image including a face of a user, extracting a feature from the input image, estimating a facial expression of the input image and an expressiveness of the facial expression of the input image corresponding to a determined expression intensity of the facial expression based on the extracted feature, normalizing the expressiveness of the facial expression of the input image to a universal expressiveness, and recognizing the facial expression based on the universal expressiveness.