Identity Authentication Model Decoupling Domain Discrepancy Features
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Solution Overview
Problem
Existing identity authentication technologies, such as face recognition, struggle with biased predictions due to domain discrepancies caused by changes like growing a beard, wearing glasses, or seasonal clothing variations, leading to inaccurate authentication results.
Innovation Solution
The proposed solution involves an identity authentication method using a two-phase generative adversarial network approach, where the first network selectively decouples domain discrepancy features based on causal relationships, and the second network performs additive adversarial training on random combinations of attribute feature vectors to eliminate irrelevant attribute dependencies, resulting in an unbiased feature representation for authentication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a neural network model is trained using a training set, then the authentication process is automated and efficient, but the model learns biased predictions due to domain discrepancies such as beard growth, glasses, or seasonal clothing changes
Solution Approach 1:
The patent segments the feature representation into multiple domain-specific feature spaces (e.g., beard domain, glasses domain, clothing domain). Each domain discrepancy is handled separately through domain-specific transformations, allowing the model to maintain accurate identity recognition while adapting to various appearance changes. This segmentation resolves the contradiction by organizing the feature space to simultaneously handle efficiency and reliability requirements.
Solution Approach 2:
The patent dynamically adjusts model parameters based on detected domain discrepancies. When changes like beard growth or glasses are detected, the system modifies the feature extraction parameters and transformation matrices specific to those domains. This parameter adaptation allows the model to maintain high authentication accuracy across different appearance conditions while preserving computational efficiency through targeted rather than comprehensive reprocessing.
2Measurement precision
If the neural network model learns to recognize all features including domain discrepancies, then comprehensive feature extraction is achieved, but biased predictions occur due to irrelevant attribute dependencies
Solution Approach 1:
The patent extracts and separates domain discrepancy features from the main identity feature representation. By identifying and isolating features related to beards, glasses, and clothing, the system removes these irrelevant attributes from the identity authentication decision process. This extraction mechanism ensures comprehensive feature capture while preventing biased predictions by excluding domain-specific variations from the final authentication logic.
Solution Approach 2:
Instead of trying to prevent the model from learning domain discrepancies, the patent inverts the approach by explicitly teaching the model to recognize and separate these features. The system trains domain-specific discriminators that identify appearance changes, then uses this knowledge to adjust the feature representation accordingly. This inversion transforms the problem from avoiding bias to actively managing and compensating for domain variations.
3Adaptability or versatility
If the model adapts to user changes like beard growth and glasses, then adaptability improves, but the complexity of the authentication system increases
Solution Approach 1:
The patent implements a nested architecture where domain-specific feature processing modules are embedded within the main identity authentication framework. Each domain (beard, glasses, clothing) has its own transformation submodule that operates within the broader identity recognition system. This nesting allows the model to adapt to various appearance changes while maintaining a structured and manageable system architecture, preventing excessive complexity through hierarchical organization.
Solution Approach 2:
The system performs preliminary domain discrepancy detection and feature separation before the main identity authentication process. By pre-identifying and handling domain-specific features in advance, the model prepares the feature representation to be more suitable for identity recognition. This preliminary action reduces the computational burden during authentication and simplifies the main processing pipeline, thereby managing system complexity while improving adaptability.
Data Source
AI summary
An identity authentication method is provided, including: acquiring a raw feature of a user; calling an identity authentication model to extract a primary attribute feature vector in the raw feature, the primary attribute feature vector being an unbiased feature representation for selectively decoupling m−1 domain discrepancy features in the raw feature, and m being an integer greater than 2; and performing unbiased identity authentication based on the primary attribute feature vector to obtain an identity authentication result.