Face Recognition Cross-Attention for Dynamic Feature Discrimination
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Solution Overview
Problem
Existing face recognition methods struggle to effectively distinguish between face images from the same identity due to the neglect of similar features and fixed representations, leading to reduced accuracy and inefficiency in feature discrimination.
Innovation Solution
The method employs a cross-attention mechanism using multi-head attention to determine global features based on local features of face images, embedding position information to enhance feature representation and improve discrimination between face pairs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a trained CNN classifier is used to extract global features from face images, then the feature extraction process is simplified and automated, but the representations of compared face images remain fixed regardless of which face image is to be compared, reducing discrimination accuracy
Solution Approach 1:
The patent transforms the fixed CNN representation into a dynamic feature extraction process by introducing a comparison-based attention mechanism. The global feature representation is dynamically adjusted based on the specific comparison task, allowing the system to adaptively emphasize relevant features for each face pair being compared, thereby resolving the contradiction between automation and precision.
Solution Approach 2:
The patent changes the parameters of feature representation by introducing attention weights that modulate the global features based on local feature comparisons. This parameter adjustment allows the same base features to produce different representations depending on the comparison context, improving discrimination accuracy while maintaining the automated extraction process.
2Measurement precision
If a feature point-based comparison method is used to discriminate face images, then sufficient discrimination between face images can be achieved, but the process becomes cumbersome requiring detection of face feature points first
Solution Approach 1:
The patent merges the global feature extraction with the local feature comparison process. Instead of separately detecting feature points and then comparing them, the method combines these operations into a unified attention-based comparison mechanism that directly computes discrimination metrics from the integrated global and local features, reducing complexity while maintaining accuracy.
Solution Approach 2:
The patent extracts the essential discrimination information from the complex feature point comparison process by using attention mechanisms to selectively focus on the most discriminative local features. This extraction approach captures the critical discrimination signals without requiring full feature point detection and comparison, simplifying the overall process.
3Measurement precision
If a contrasting convolution mechanism is used to extract different features between face image pairs, then face recognition accuracy is enhanced, but the action of similar features between compared face pairs is ignored
Solution Approach 1:
The patent applies local quality by treating different and similar features with different processing weights. The attention mechanism selectively enhances the contribution of discriminative different features while preserving and appropriately weighting the similar features, rather than uniformly processing all features. This allows the system to focus on distinguishing characteristics while maintaining information about similarities that may be important for identity verification.
Data Source
AI summary
A method with face recognition includes: determining a first global feature of a first face image and a first global feature of a second face image based on a local feature of the first face image and a local feature of the second face image, respectively; determining a final global feature of the first face image based on the first global feature of the first face image and a second global feature of the first face image; determining a final global feature of the second face image based on the first global feature and a second global feature of the second face image; and recognizing the first face image and the second face image based on the final global feature of the first face image and the final global feature of the second face image, wherein the second global feature of the first face image is determined based on the local feature of the first face image, and the second global feature of the second face image is determined based on the local feature of the second face image.