Biometric Fraud Detection Using Multi-Wavelength Neural Encoding
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Solution Overview
Problem
Biometric recognition systems using single-image analysis in the visible spectrum are susceptible to fraud and inadequate in detecting new fraud techniques, especially when faced with new poses, expressions, or fraud technologies.
Innovation Solution
The method involves encoding images from different wavelength bands using neural networks to generate vector representations, then computing similarity measures between these representations to detect fraud, with encoders trained to maximize similarity for authentic images and minimize similarity for fraudulent images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single image in the visible spectrum is used for biometric recognition, then the system is simple to implement, but it is susceptible to fraud and cannot detect new fraud techniques
Solution Approach 1:
The patent transitions from analyzing images in a single wavelength dimension (visible spectrum) to multiple wavelength dimensions (visible and infrared). This dimensional expansion enables the system to capture different physiological characteristics of authentic versus fraudulent subjects, thereby improving fraud detection capability without significantly increasing system complexity
Solution Approach 2:
The patent changes the spectral parameter by acquiring images in different wavelength bands (visible and infrared). This parameter change allows the system to exploit the different optical properties of real human tissue versus fraudulent materials (such as masks or photographs), enabling detection of previously undetectable fraud techniques
2Reliability
If multiple wavelength bands are used for fraud detection, then fraud detection capability is improved, but the system becomes more complex
Solution Approach 1:
The patent employs a unified neural network architecture that processes both visible and infrared images simultaneously. This multi-functional system performs both authentication and fraud detection using a single model, rather than requiring separate systems for each wavelength band, thereby managing complexity while maintaining improved detection capability
Solution Approach 2:
The patent merges the processing of visible and infrared images into a single joint learning framework. By combining the information from multiple wavelength bands within one neural network, the system achieves better fraud detection than separate systems would, while avoiding the complexity of coordinating multiple independent systems
3Measurement precision
If classifiers are trained on known fraud techniques and poses, then they perform well on training data, but their results are unpredictable when confronted with new poses, expressions, or fraud technology
Solution Approach 1:
The patent performs preliminary action by training the neural network on a diverse dataset that includes multiple poses, expressions, and fraud techniques before deployment. This pre-training with varied data enables the system to generalize better to unseen fraud techniques and poses, improving adaptability while maintaining reasonable accuracy on known patterns
Data Source
AI summary
A method for determining fraud in a biometric recognition system including obtaining a first image of a region of interest of a subject in a first wavelength band; obtaining a second image of the region of interest of the subject in a second wavelength band; encoding the first image by means of a first neural encoder, to obtain a first vector representation of the first image; encoding the second image by means of a second neural encoder, to obtain a second vector representation of the second image; computing a measure of similarity between the first vector representation and the second vector representation; and determining a fraud if the similarity measure is below a predefined threshold.