Multi-Spectral Eye Authentication via Neural Network Fusion
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Solution Overview
Problem
Conventional identity authentication systems rely on hand-crafted features, which may be less effective in discriminating between users, and lack the use of multi-spectral imaging, resulting in lower accuracy in user identification.
Innovation Solution
An authentication system that processes multi-spectral images of the eye using an encoder neural network to generate a fused image, determining user identity based on features derived from multiple channels such as red, green, blue, infrared, and ultraviolet channels, employing machine learning techniques to learn discriminative features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If hand-crafted features are used for identity authentication, then the system complexity is reduced, but the discrimination accuracy between users deteriorates
Solution Approach 1:
The patent replaces hand-crafted feature extraction (mechanical/systematic approach) with a neural network-based automatic feature learning system. The encoder neural network automatically learns discriminative features from multi-spectral eye images, substituting the manual feature engineering process with an intelligent learning system that achieves superior discrimination accuracy.
Solution Approach 2:
The patent combines multiple spectral channels (visible light and infrared) to create a composite multi-spectral image representation. This composite approach integrates information from different spectral domains, enabling the neural network to learn more comprehensive and discriminative features for user identification.
2Device complexity
If single-spectral imaging is used, then the device complexity is reduced, but the user identification accuracy deteriorates
Solution Approach 1:
The patent extends the imaging system from single-spectral (one dimension) to multi-spectral (multiple dimensions) by incorporating both visible light and infrared channels. This dimensional expansion provides richer information about eye characteristics, enabling more accurate user identification through the neural network's ability to process multi-dimensional spectral data.
3Productivity
If conventional single-channel processing is used, then the processing speed is maintained, but the feature discrimination capability deteriorates
Solution Approach 1:
The patent merges multiple spectral channels (red, green, blue, infrared) into a unified multi-spectral representation that is processed by the encoder neural network. This merging allows the system to maintain efficient processing while simultaneously leveraging discriminative features from multiple spectral domains, achieving both speed and accuracy.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining the identity of a user. In one aspect, a method comprises: obtaining a multi-spectral image that depicts an eye of a user, wherein the multi-spectral image comprises a plurality of registered two-dimensional channels, and each two-dimensional channel corresponds to a different spectrum of the multi-spectral image; processing the multi-spectral image using an encoder neural network to generate a fused image, wherein the fused image has a single two-dimensional channel; determining a set of features characterizing the eye of the user from the fused image; and determining an identity of the user based at least in part on the set of features characterizing the eye of the user.