Neural Network Authentication Device for Secure Biometric Verification
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Solution Overview
Problem
Current authentication methods using identification documents face challenges such as large data volumes, security risks, and inefficiencies in comparing biometric data, particularly in border control systems, where personal data is sensitive and difficult to manage securely.
Innovation Solution
An authentication device and method utilizing a neural network to process and compare digital reference features from a stored digital reference head model with live-captured data, generating modified response data that is less data-intensive and securely processed, allowing for efficient and secure person authentication without exposing raw data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If raw biometric data is stored in plain text on the smartphone for authentication comparison, then authentication can be performed, but security is compromised and personal data is exposed
Solution Approach 1:
The patent extracts only the essential authentication result (match/no match) from the comparison process, rather than storing or processing the complete raw biometric data. The smartphone reads authentication results from the ID document without accessing the underlying personal data, thereby achieving authentication while eliminating security risks associated with data exposure.
Solution Approach 2:
The ID document's secure memory acts as an intermediary that performs the authentication comparison internally and returns only the authentication result. This mediator prevents direct access to personal data while enabling verification, resolving the contradiction between authentication capability and data security.
2Ease of operation
If complete biometric data is read from the ID document and stored on the smartphone, then authentication comparison can be performed, but data storage requirements increase and processing complexity rises
Solution Approach 1:
The system extracts and processes only the minimal necessary information for authentication - the authentication result - rather than handling complete biometric datasets. This reduces data volume from megabytes of raw biometric data to a simple binary authentication outcome, eliminating storage and processing burdens.
Solution Approach 2:
Instead of having the smartphone read and process all biometric data from the ID document, the approach is inverted: the ID document's secure environment performs the comparison and returns only the authentication result. This reverses the data flow direction, minimizing data transfer and processing requirements on the smartphone.
3Ease of operation
If authorities can read personal data from the ID document for authentication, then verification can be performed, but the risk of data compromise and loss increases
Solution Approach 1:
The system extracts and retains only the authentication capability within the ID document's secure memory, separating this function from personal data storage. Authorities can perform authentication by reading the authentication result without accessing personal data, maintaining security while enabling verification functionality.
Solution Approach 2:
The secure authentication memory in the ID document serves as an intermediary that handles all authentication operations internally. It receives authentication requests, performs comparisons using stored reference data, and returns results without exposing personal information to authorities or external systems, thus protecting data while enabling verification.
Data Source
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AI summary
The present disclosure relates to an authentication device (200) for authenticating a person (103) by means of an identification document (100) which has a storage area (110) in which a reference feature set (101) is stored, which has digital reference features (107) of a digital reference head model, wherein the reference feature set (101) comprises a response (212) using a trained neural network (205), with: an image acquisition device (201) which is configured to capture a head image (202) of the person;a processor (203) which is trained to generate a digital comparison head model (204) based on the recorded head image of the person, to provide a comparison feature set (206) with digital comparison features (207) as a response (210) of the trained neural network based on the digital comparison head model (204), and furthermore to determine a difference (208) between the digital reference features and the digital comparison features in order to authenticate the person. (Fig. 1);