IR User Authentication Accuracy via Environmental Feature Matching
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Solution Overview
Problem
Existing user authentication methods using infrared (IR) images struggle with accurately authenticating users due to variations in environmental conditions such as capturing distance, light direction, and indoor vs. outdoor settings, leading to incorrect authentication and high false acceptance rates.
Innovation Solution
A user authentication method that captures environmental information, extracts feature vectors from IR images, and selects matching feature vectors from an enrollment database to authenticate users based on similarity calculations, considering factors like season, climate, and capturing conditions, and assigns weights to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If environmental conditions are not considered in authentication, then the authentication process is simple and fast, but authentication accuracy decreases and false acceptance rate increases
Solution Approach 1:
The system performs preliminary actions by capturing environmental information (temperature, humidity, light conditions, capturing distance) during the enrollment phase and storing it alongside feature vectors. This preliminary capture of environmental data allows the authentication system to later select reference feature vectors that match the current environmental conditions, thereby improving authentication accuracy without adding complex real-time environmental analysis during the authentication moment itself
Solution Approach 2:
The system changes the parameter set used for authentication by incorporating environmental parameters (temperature, humidity, light conditions, capturing distance) into the feature vector comparison process. Instead of comparing only biometric features, the system now compares biometric features along with their associated environmental parameters, allowing for more accurate matching by accounting for environmental variations that affect biometric readings
2Reliability
If environmental information is captured and processed, then authentication accuracy improves, but processing time and computational load increase
Solution Approach 1:
Environmental information is captured and stored during the enrollment phase rather than being processed in real-time during authentication. The system pre-processes environmental data and associates it with reference feature vectors, so that during authentication, the system only needs to retrieve and compare pre-processed environmental parameters, significantly reducing computational load and processing time
Solution Approach 2:
The system creates copies of environmental information and stores them alongside feature vectors in the database. Instead of re-measuring and re-processing environmental conditions during each authentication, the system retrieves stored environmental copies that match the current conditions, reducing real-time processing requirements while maintaining authentication accuracy
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances user authentication accuracy by considering environmental conditions, reducing false acceptance rates and improving recognition performance by ensuring feature vectors are matched under similar capturing conditions, thus improving the reliability of IR-based user authentication systems.
Implementation Method 1
an infrared ray (IR) sensor configured to capture an input image of the user
Data Source
AI summary
Provided is a user authentication method and apparatus that obtains first environmental information indicating an environmental condition in which an input image of a user is captured, extracts a first feature vector from the input image, selects a second feature vector including second environmental information that matches the first environmental information from enrolled feature vectors in an enrollment database (DB), and authenticates the user based on the first feature vector and the second feature vector.